Data preprocessing for heart disease classification: A systematic literature review
•A systematic review on the use of data preprocessing techniques for heart disease classification purpose was conducted.•A total of 49 studies published between January 2000 and June 2019 were selected and analyzed considering four review questions.•A significant number of selected studies were devo...
Saved in:
| Published in | Computer methods and programs in biomedicine Vol. 195; p. 105635 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Ireland
Elsevier B.V
01.10.2020
|
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | •A systematic review on the use of data preprocessing techniques for heart disease classification purpose was conducted.•A total of 49 studies published between January 2000 and June 2019 were selected and analyzed considering four review questions.•A significant number of selected studies were devoted to data reduction task. Feature•selection was the most used data reduction sub-task. Moreover, Data cleaning was a common task in classification for cardiology and it dealt mainly with missing values and noise elimination in cardiac datasets.•In general, preprocessing either maintained or improved the performance of heart disease•Researchers concentrated more on improving the accuracy rate of the models developed while neglecting other aspects such as time complexity and comprehensibility.
Early detection of heart disease is an important challenge since 17.3 million people yearly lose their lives due to heart diseases. Besides, any error in diagnosis of cardiac disease can be dangerous and risks an individual's life. Accurate diagnosis is therefore critical in cardiology. Data Mining (DM) classification techniques have been used to diagnosis heart diseases but still limited by some challenges of data quality such as inconsistencies, noise, missing data, outliers, high dimensionality and imbalanced data. Data preprocessing (DP) techniques were therefore used to prepare data with the goal of improving the performance of heart disease DM based prediction systems.
The purpose of this study is to review and summarize the current evidence on the use of preprocessing techniques in heart disease classification as regards: (1) the DP tasks and techniques most frequently used, (2) the impact of DP tasks and techniques on the performance of classification in cardiology, (3) the overall performance of classifiers when using DP techniques, and (4) comparisons of different combinations classifier-preprocessing in terms of accuracy rate.
A systematic literature review is carried out, by identifying and analyzing empirical studies on the application of data preprocessing in heart disease classification published in the period between January 2000 and June 2019. A total of 49 studies were therefore selected and analyzed according to the aforementioned criteria.
The review results show that data reduction is the most used preprocessing task in cardiology, followed by data cleaning. In general, preprocessing either maintained or improved the performance of heart disease classifiers. Some combinations such as (ANN + PCA), (ANN + CHI) and (SVM + PCA) are promising terms of accuracy. However the deployment of these models in real-world diagnosis decision support systems is subject to several risks and limitations due to the lack of interpretation. |
|---|---|
| AbstractList | Early detection of heart disease is an important challenge since 17.3 million people yearly lose their lives due to heart diseases. Besides, any error in diagnosis of cardiac disease can be dangerous and risks an individual's life. Accurate diagnosis is therefore critical in cardiology. Data Mining (DM) classification techniques have been used to diagnosis heart diseases but still limited by some challenges of data quality such as inconsistencies, noise, missing data, outliers, high dimensionality and imbalanced data. Data preprocessing (DP) techniques were therefore used to prepare data with the goal of improving the performance of heart disease DM based prediction systems.CONTEXTEarly detection of heart disease is an important challenge since 17.3 million people yearly lose their lives due to heart diseases. Besides, any error in diagnosis of cardiac disease can be dangerous and risks an individual's life. Accurate diagnosis is therefore critical in cardiology. Data Mining (DM) classification techniques have been used to diagnosis heart diseases but still limited by some challenges of data quality such as inconsistencies, noise, missing data, outliers, high dimensionality and imbalanced data. Data preprocessing (DP) techniques were therefore used to prepare data with the goal of improving the performance of heart disease DM based prediction systems.The purpose of this study is to review and summarize the current evidence on the use of preprocessing techniques in heart disease classification as regards: (1) the DP tasks and techniques most frequently used, (2) the impact of DP tasks and techniques on the performance of classification in cardiology, (3) the overall performance of classifiers when using DP techniques, and (4) comparisons of different combinations classifier-preprocessing in terms of accuracy rate.OBJECTIVEThe purpose of this study is to review and summarize the current evidence on the use of preprocessing techniques in heart disease classification as regards: (1) the DP tasks and techniques most frequently used, (2) the impact of DP tasks and techniques on the performance of classification in cardiology, (3) the overall performance of classifiers when using DP techniques, and (4) comparisons of different combinations classifier-preprocessing in terms of accuracy rate.A systematic literature review is carried out, by identifying and analyzing empirical studies on the application of data preprocessing in heart disease classification published in the period between January 2000 and June 2019. A total of 49 studies were therefore selected and analyzed according to the aforementioned criteria.METHODA systematic literature review is carried out, by identifying and analyzing empirical studies on the application of data preprocessing in heart disease classification published in the period between January 2000 and June 2019. A total of 49 studies were therefore selected and analyzed according to the aforementioned criteria.The review results show that data reduction is the most used preprocessing task in cardiology, followed by data cleaning. In general, preprocessing either maintained or improved the performance of heart disease classifiers. Some combinations such as (ANN + PCA), (ANN + CHI) and (SVM + PCA) are promising terms of accuracy. However the deployment of these models in real-world diagnosis decision support systems is subject to several risks and limitations due to the lack of interpretation.RESULTSThe review results show that data reduction is the most used preprocessing task in cardiology, followed by data cleaning. In general, preprocessing either maintained or improved the performance of heart disease classifiers. Some combinations such as (ANN + PCA), (ANN + CHI) and (SVM + PCA) are promising terms of accuracy. However the deployment of these models in real-world diagnosis decision support systems is subject to several risks and limitations due to the lack of interpretation. •A systematic review on the use of data preprocessing techniques for heart disease classification purpose was conducted.•A total of 49 studies published between January 2000 and June 2019 were selected and analyzed considering four review questions.•A significant number of selected studies were devoted to data reduction task. Feature•selection was the most used data reduction sub-task. Moreover, Data cleaning was a common task in classification for cardiology and it dealt mainly with missing values and noise elimination in cardiac datasets.•In general, preprocessing either maintained or improved the performance of heart disease•Researchers concentrated more on improving the accuracy rate of the models developed while neglecting other aspects such as time complexity and comprehensibility. Early detection of heart disease is an important challenge since 17.3 million people yearly lose their lives due to heart diseases. Besides, any error in diagnosis of cardiac disease can be dangerous and risks an individual's life. Accurate diagnosis is therefore critical in cardiology. Data Mining (DM) classification techniques have been used to diagnosis heart diseases but still limited by some challenges of data quality such as inconsistencies, noise, missing data, outliers, high dimensionality and imbalanced data. Data preprocessing (DP) techniques were therefore used to prepare data with the goal of improving the performance of heart disease DM based prediction systems. The purpose of this study is to review and summarize the current evidence on the use of preprocessing techniques in heart disease classification as regards: (1) the DP tasks and techniques most frequently used, (2) the impact of DP tasks and techniques on the performance of classification in cardiology, (3) the overall performance of classifiers when using DP techniques, and (4) comparisons of different combinations classifier-preprocessing in terms of accuracy rate. A systematic literature review is carried out, by identifying and analyzing empirical studies on the application of data preprocessing in heart disease classification published in the period between January 2000 and June 2019. A total of 49 studies were therefore selected and analyzed according to the aforementioned criteria. The review results show that data reduction is the most used preprocessing task in cardiology, followed by data cleaning. In general, preprocessing either maintained or improved the performance of heart disease classifiers. Some combinations such as (ANN + PCA), (ANN + CHI) and (SVM + PCA) are promising terms of accuracy. However the deployment of these models in real-world diagnosis decision support systems is subject to several risks and limitations due to the lack of interpretation. Early detection of heart disease is an important challenge since 17.3 million people yearly lose their lives due to heart diseases. Besides, any error in diagnosis of cardiac disease can be dangerous and risks an individual's life. Accurate diagnosis is therefore critical in cardiology. Data Mining (DM) classification techniques have been used to diagnosis heart diseases but still limited by some challenges of data quality such as inconsistencies, noise, missing data, outliers, high dimensionality and imbalanced data. Data preprocessing (DP) techniques were therefore used to prepare data with the goal of improving the performance of heart disease DM based prediction systems. The purpose of this study is to review and summarize the current evidence on the use of preprocessing techniques in heart disease classification as regards: (1) the DP tasks and techniques most frequently used, (2) the impact of DP tasks and techniques on the performance of classification in cardiology, (3) the overall performance of classifiers when using DP techniques, and (4) comparisons of different combinations classifier-preprocessing in terms of accuracy rate. A systematic literature review is carried out, by identifying and analyzing empirical studies on the application of data preprocessing in heart disease classification published in the period between January 2000 and June 2019. A total of 49 studies were therefore selected and analyzed according to the aforementioned criteria. The review results show that data reduction is the most used preprocessing task in cardiology, followed by data cleaning. In general, preprocessing either maintained or improved the performance of heart disease classifiers. Some combinations such as (ANN + PCA), (ANN + CHI) and (SVM + PCA) are promising terms of accuracy. However the deployment of these models in real-world diagnosis decision support systems is subject to several risks and limitations due to the lack of interpretation. |
| ArticleNumber | 105635 |
| Author | Benhar, H. Fernández-Alemán, J.L. Idri, A. |
| Author_xml | – sequence: 1 givenname: H. surname: Benhar fullname: Benhar, H. email: benhar.houda@gmail.com organization: Software Project Management Research Team, ENSIAS, University Mohammed V in Rabat, Morocco – sequence: 2 givenname: A. surname: Idri fullname: Idri, A. email: ali.idri@um5.ac.ma, ali.idri@um5.ac.com organization: Software Project Management Research Team, ENSIAS, University Mohammed V in Rabat, Morocco – sequence: 3 givenname: J.L. surname: Fernández-Alemán fullname: Fernández-Alemán, J.L. email: aleman@um.es organization: Department of Informatics and Systems, Faculty of Computer Science, University of Murcia, Spain |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32652383$$D View this record in MEDLINE/PubMed |
| BookMark | eNqFkUtvWyEQRlGVqHEef6CLimU31-VhHjfqJnKbNJKlLJKuEYahwbkPF3Aq__vi2tlkkawQ8J2Z4XCKjoZxAIQ-UTKlhMqvq6nr18spI2x3ICQXH9CEasUaJaQ4QpMaahsmiTpBpzmvCCFMCPkRnXAmBeOaT9D9d1ssXidYp9FBznH4jcOY8CPYVLCPGWwG7Dpbr0J0tsRxuMRXOG9zgb5uHe5igWTLJgFO8Bzh7zk6DrbLcHFYz9Cv6x8P85_N4u7mdn61aNxM8dKEIGlrpWduVmfnaumDVIq1Xlql6FIHooVgmtBWM-Fly6l1SnIqlNct1YGfoS_7unX2PxvIxfQxO-g6O8C4yYbNGK9aapca_XyIbpY9eLNOsbdpa15E1IDeB1wac04QjIvl_2tLsrEzlJidc7MyO-dm59zsnVeUvUJfqr8JfdtDUAVVaclkF2Fw4GMCV4wf49v45SvcdXGo_9M9wfY9-B-TI6yo |
| CitedBy_id | crossref_primary_10_1016_j_ijcard_2024_132757 crossref_primary_10_1049_sil2_12232 crossref_primary_10_3390_math11224681 crossref_primary_10_5334_dsj_2023_031 crossref_primary_10_1016_j_chemolab_2023_104933 crossref_primary_10_3390_bioengineering10121386 crossref_primary_10_3390_s22208073 crossref_primary_10_1016_j_datak_2024_102339 crossref_primary_10_1016_j_eswa_2023_120109 crossref_primary_10_1007_s11063_021_10667_8 crossref_primary_10_1155_2022_9580896 crossref_primary_10_1038_s41746_023_00757_3 crossref_primary_10_1080_21681163_2020_1811159 crossref_primary_10_1039_D4YA00570H crossref_primary_10_1109_ACCESS_2024_3418629 crossref_primary_10_1007_s42979_023_01809_x crossref_primary_10_4103_cmrp_cmrp_51_24 crossref_primary_10_1109_ACCESS_2024_3510035 crossref_primary_10_1088_1742_6596_1950_1_012081 crossref_primary_10_1007_s00500_023_08330_6 crossref_primary_10_3389_fdata_2024_1393758 crossref_primary_10_32604_jai_2024_050277 crossref_primary_10_1016_j_bbrc_2024_149468 crossref_primary_10_1007_s40098_024_01032_2 crossref_primary_10_3390_ijerph20146404 crossref_primary_10_3390_app11178240 crossref_primary_10_1007_s10115_022_01772_8 crossref_primary_10_1186_s40537_025_01116_7 crossref_primary_10_3390_biomed2040031 crossref_primary_10_1016_j_undsp_2021_12_003 crossref_primary_10_1016_j_eswa_2023_121608 crossref_primary_10_3390_ijerph192215167 crossref_primary_10_1007_s12046_023_02105_3 crossref_primary_10_1021_acsami_1c23604 crossref_primary_10_3390_info14040210 crossref_primary_10_3389_fmed_2025_1506363 crossref_primary_10_1109_ACCESS_2022_3208715 crossref_primary_10_1016_j_artmed_2022_102289 crossref_primary_10_1016_j_jcomm_2024_100438 crossref_primary_10_2459_JCM_0000000000001497 crossref_primary_10_3233_IDA_230140 crossref_primary_10_3390_s24227102 crossref_primary_10_1016_j_undsp_2023_01_001 crossref_primary_10_3233_JIFS_220061 crossref_primary_10_3390_math10244633 crossref_primary_10_3390_healthcare13010037 crossref_primary_10_1007_s00508_021_01970_4 crossref_primary_10_3390_a17020078 crossref_primary_10_3390_epigenomes6010001 crossref_primary_10_1080_01969722_2022_2080338 crossref_primary_10_18185_erzifbed_848004 crossref_primary_10_1007_s10845_024_02554_5 crossref_primary_10_1016_j_biosystemseng_2021_11_002 |
| Cites_doi | 10.1377/hlthaff.26.1.13 10.1038/nature14539 10.1016/j.asoc.2013.09.020 10.1109/TIT.1967.1053964 10.1088/1742-6596/1087/6/062032 10.1007/BF00116251 10.1016/j.dss.2016.10.005 10.1016/j.knosys.2019.104923 10.1016/j.compbiomed.2010.11.003 10.1093/eurheartj/ehz056 10.3844/jcssp.2006.735.739 10.1007/978-3-642-14834-7_40 10.1016/S1532-0464(03)00034-0 10.1016/S1352-2310(97)00447-0 10.1007/978-3-319-56991-8_31 10.1016/j.cmpb.2005.08.001 10.1016/j.tele.2018.11.007 10.1007/s11633-014-0778-5 10.1016/j.knosys.2015.05.014 10.1016/j.cmpb.2013.08.002 10.1016/j.ijmedinf.2016.09.005 10.1109/ICTAI.2004.93 10.1016/j.tcs.2011.05.043 10.1109/TBME.2006.873753 10.1016/j.infsof.2014.07.013 10.1016/j.media.2017.11.008 10.1016/j.ejor.2005.07.023 10.1016/j.jss.2016.05.016 10.1007/s00034-014-9864-8 10.4015/S1016237217500430 10.1007/s11704-016-5203-5 10.1016/j.eswa.2009.09.064 10.1007/978-81-322-1665-0_59 10.1016/j.infsof.2015.03.007 10.1109/TITB.2008.923147 10.1016/j.ins.2013.07.007 10.1016/j.neucom.2011.10.045 10.1016/j.artmed.2010.05.002 10.2478/v10136-012-0031-x 10.1016/j.compbiomed.2017.09.011 10.1016/j.eswa.2016.12.035 10.1016/j.jbi.2017.05.011 10.1016/j.artmed.2008.11.004 10.2307/2669967 10.1016/j.bspc.2017.12.004 10.1016/j.eswa.2017.09.022 10.1016/j.cmpb.2019.104992 10.17485/ijst/2016/v9i45/102290 10.1109/81.739259 10.1016/j.protcy.2012.02.068 10.1016/j.artmed.2008.04.007 10.24138/jcomss.v11i1.114 10.1016/j.eswa.2014.01.011 10.1016/j.jss.2016.04.058 10.1016/j.eswa.2010.06.065 10.3934/bioeng.2017.1.179 10.1016/j.eswa.2010.05.078 10.21037/mhealth.2017.09.01 10.1080/08839514.2015.1051887 10.1016/j.eswa.2009.01.041 10.1001/jamacardio.2018.0136 10.1080/08839514.2016.1193719 10.1148/radiol.2018180887 10.1016/j.cmpb.2018.05.007 10.1017/S0269888906000737 10.1016/j.jcmg.2018.01.020 10.1007/978-3-642-32909-8_35 10.1038/s41746-017-0013-1 10.1016/j.jbi.2017.01.001 10.1007/s10916-014-0048-7 10.1109/TITB.2010.2094197 10.1007/s10916-018-1134-z 10.1016/S0261-5177(99)00067-9 10.1016/j.jbi.2012.04.013 10.1016/j.compbiomed.2013.10.016 |
| ContentType | Journal Article |
| Copyright | 2020 Elsevier B.V. Copyright © 2020 Elsevier B.V. All rights reserved. |
| Copyright_xml | – notice: 2020 Elsevier B.V. – notice: Copyright © 2020 Elsevier B.V. All rights reserved. |
| DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM 7X8 |
| DOI | 10.1016/j.cmpb.2020.105635 |
| DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed MEDLINE - Academic |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic MEDLINE |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine |
| EISSN | 1872-7565 |
| ExternalDocumentID | 32652383 10_1016_j_cmpb_2020_105635 S0169260720314681 |
| Genre | Systematic Review Journal Article |
| GroupedDBID | --- --K --M -~X .1- .DC .FO .GJ .~1 0R~ 1B1 1P~ 1RT 1~. 1~5 29F 4.4 457 4G. 53G 5GY 5RE 5VS 7-5 71M 8P~ 9JN AAEDT AAEDW AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AATTM AAXKI AAXUO AAYFN AAYWO ABBOA ABFNM ABJNI ABMAC ABMZM ABWVN ABXDB ACDAQ ACGFS ACIEU ACIUM ACNNM ACRLP ACRPL ACVFH ACZNC ADBBV ADCNI ADEZE ADJOM ADMUD ADNMO AEBSH AEIPS AEKER AENEX AEUPX AEVXI AFJKZ AFPUW AFRHN AFTJW AFXIZ AGCQF AGHFR AGQPQ AGUBO AGYEJ AHHHB AHZHX AIALX AIEXJ AIGII AIIUN AIKHN AITUG AJRQY AJUYK AKBMS AKRWK AKYEP ALMA_UNASSIGNED_HOLDINGS AMRAJ ANKPU ANZVX AOUOD APXCP ASPBG AVWKF AXJTR AZFZN BKOJK BLXMC BNPGV CS3 DU5 EBS EFJIC EFKBS EJD EO8 EO9 EP2 EP3 F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN G-2 G-Q GBLVA GBOLZ HLZ HMK HMO HVGLF HZ~ IHE J1W KOM LG9 M29 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- ROL RPZ SAE SBC SDF SDG SEL SES SEW SPC SPCBC SSH SSV SSZ T5K UHS WUQ XPP Z5R ZGI ZY4 ~G- AACTN AAIAV ABLVK ABTAH ABYKQ AFKWA AJBFU AJOXV AMFUW EFLBG LCYCR RIG AAYXX AFCTW AGRNS CITATION CGR CUY CVF ECM EIF NPM 7X8 |
| ID | FETCH-LOGICAL-c473t-ff619a6d2c456337bdf67729d6a771b8f085528019825d6931ac763157d8918f3 |
| IEDL.DBID | .~1 |
| ISSN | 0169-2607 1872-7565 |
| IngestDate | Fri Jul 11 02:13:15 EDT 2025 Mon Jul 21 05:57:37 EDT 2025 Tue Jul 01 02:41:03 EDT 2025 Thu Apr 24 23:01:28 EDT 2025 Fri Feb 23 02:47:09 EST 2024 Tue Aug 26 16:33:32 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Literature review Datamining Cardiology Cardiac datasets Data preprocessing |
| Language | English |
| License | Copyright © 2020 Elsevier B.V. All rights reserved. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c473t-ff619a6d2c456337bdf67729d6a771b8f085528019825d6931ac763157d8918f3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 ObjectType-Review-3 content type line 23 ObjectType-Undefined-4 |
| PMID | 32652383 |
| PQID | 2423056619 |
| PQPubID | 23479 |
| ParticipantIDs | proquest_miscellaneous_2423056619 pubmed_primary_32652383 crossref_citationtrail_10_1016_j_cmpb_2020_105635 crossref_primary_10_1016_j_cmpb_2020_105635 elsevier_sciencedirect_doi_10_1016_j_cmpb_2020_105635 elsevier_clinicalkey_doi_10_1016_j_cmpb_2020_105635 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | October 2020 2020-10-00 2020-Oct 20201001 |
| PublicationDateYYYYMMDD | 2020-10-01 |
| PublicationDate_xml | – month: 10 year: 2020 text: October 2020 |
| PublicationDecade | 2020 |
| PublicationPlace | Ireland |
| PublicationPlace_xml | – name: Ireland |
| PublicationTitle | Computer methods and programs in biomedicine |
| PublicationTitleAlternate | Comput Methods Programs Biomed |
| PublicationYear | 2020 |
| Publisher | Elsevier B.V |
| Publisher_xml | – name: Elsevier B.V |
| References | Karabulut, Özel, İbrikçi (bib0102) 2012; 1 García, Luengo, Herrera (bib0012) 2015 Jabbar, Deekshatulu, Chandra (bib0031) Mar. 2015 Lecun, Bengio, Hinton (bib0030) 2015 Jabbar, Deekshatulu, Chandra (bib0055) 2013; 13 Idri, Chlioui, El Ouassif (bib0008) 2018 Pizzi (bib0065) 2011; 412 Melgani, Bazi (bib0067) 2008; 12 Yu, Chen (bib0084) 2009; 46 Tison (bib0039) 2018 Cover, Hart (bib0125) Jan. 1967; 13 Liu (bib0044) Sep. 2018; 1087 Dag, Oztekin, Yucel, Bulur, Megahed (bib0016) 2017; 94 Benhar, Idri, Hosni (bib0024) 2020 Shao, Hou, Chiu (bib0003) 2014; 14 Idri, Hosni, Abran (bib0050) 2016; 118 (accessed Oct. 08, 2019). J. Mohamad-Saleh and B. S. Hoyle, “Improved Neural Network Performance Using Principal Component Analysis on Matlab.” 2008. Han, Kamber, Pei (bib0124) 2012 Gardner, Dorling (bib0119) Aug. 1998; 32 Pławiak (bib0068) 2018 Anbarasi, Anupriya, Iyengar (bib0069) 2010 . Mustaqeem, Anwar, Majid, Khan (bib0005) 2017 Haixiang, Yijing, Shang, Mingyun, Yuanyue, Bing (bib0017) 2017; 73 Bolón-Canedo, Sánchez-Maroño, Alonso-Betanzos (bib0074) Sep. 2015; 86 Journal Citation Reports. Fayyad, Piatetsky-Shapiro, Smyth (bib0014) 1996; 17 Li, Li, Zhu, Kambhamettu (bib0115) 2002 Jiang, Zhang, Zhao, Albayrak (bib0037) 2006 Al Shalabi, Shaaban, Kasasbeh (bib0080) 2006; 2 Kohli, Arora (bib0118) 2018 Jerez (bib0060) 2010; 50 Hejazi, Al-Haddad, Singh, Hashim, Aziz (bib0073) 2015; 29 Asl, Setarehdan, Mohebbi (bib0072) 2008; 44 “World Health Organization,” 2019. (accessed Nov. 26, 2019). Vivekanandan, Sriman (bib0114) 2017; 90 El Idrissi, Idri, Bakkoury (bib0007) 2018 Dreiseitl, Ohno-Machado (bib0098) Oct. 2002; 35 Wang, Chiang, Hsu, Yang (bib0035) Sep. 2013; 116 Kadi, Idri, Fernandez-Aleman (bib0006) Aug. 2017; 1 Song, Lee, Cho, Lee, Yoo (bib0032) 2005; 3 Kolukisa (bib0066) 2019 Gao, Yang, Xing, Xu (bib0107) Jul. 2012 Subasi, Gursoy (bib0104) 2010; 37 Krittanawong (bib0029) 2019 Law (bib0120) Aug. 2000; 21 Zhou, Tian, Lim (bib0082) 2015; 26 Betancur (bib0042) 2018 Al Khaldy, Kambhampati (bib0095) 2018 Zadeh (bib0094) 1999; 46 Guyon, Steve, Masoud, Lotfi (bib0018) 2006; 207 Loh, Then (bib0027) 2017 Abawajy, Kelarev, Chowdhury (bib0061) 2013; 112 Idri, Amazal, Abran (bib0052) 2015; 58 Xu (bib0105) 2006; 2 Janardhanan, L., Sabika (bib0103) . 2015; 11 Bachri, Kusnadi, Nurhayati (bib0100) 2017; 8 Almuhaideb, Menai (bib0057) 2016; 10 Pławiak, Acharya (bib0086) 2019 Vivencio, Hruschka, Do Carmo Nicoletti, Dos Santos, Galvão (bib0097) 2007 Poolsawad, Moore, Kambhampati, Cleland (bib0022) 2014; 11 Petersen, Vakkalanka, Kuzniarz (bib0047) 2015; 64 Aziz, Verma, Srivastava (bib0071) 2017 Kadi, Idri, Fernandez-Aleman (bib0010) 2017; 97 Patil, Joshi, Toshniwal (bib0023) 2010; 94 Zaki, Meira (bib0075) 2014 Banerjee, Bhattacharya, Alam (bib0064) 2018 Rajesh, Dhuli (bib0116) 2018 Fitkov-Norris, Vahid, Hand (bib0090) 2012 rankings (accessed Dec. 02, 2019). Yilmaz, Inan, Uzer (bib0002) 2014; 38 Sardi, Idri, Fernández-Alemán (bib0051) Jul. 2017; 71 Amin, Chiam, Varathan (bib0117) 2019 Abraham, Simha, Iyengar (bib0076) Dec. 2007 Tuncer, Dogan, Pławiak, Rajendra Acharya (bib0085) 2019 MTW, Motoda (bib0025) 1999 Amato, López, Peña-Méndez, Vaňhara, Hampl, Havel (bib0099) 2013; 11 Vapnik (bib0122) 2000 García, Luengo, Herrera (bib0070) 2015; 72 Patil, Joshi, Toshniwal (bib0109) 2010; 37 Polat, Gunes (bib0113) 2009; 36 Statlog (Heart) Data Set. Babaoglu, Findik, Ülker (bib0111) 2010; 37 Tang, Khoshgoftaar (bib0108) 2004 Madani, Arnaout, Mofrad, Arnaout (bib0040) 2018 Jabbar, Deekshatulu, Chandra (bib0083) Dec. 2013 Crone, Lessmann, Stahlbock (bib0078) 2006 Zreik (bib0043) 2018 Kutlu, Kuntalp (bib0081) 2011; 41 Sasikala, Appavu, Geetha (bib0034) 2014; 243 Alickovic, Subasi (bib0036) 2014; 34 “No Title, ” Arrhythmia Data Set. Bhargava, Sharma, Bhargava, Mathuria (bib0126) 2013; 3 Abdel-Aal (bib0110) 2005; 80 Miotto, Wang, Wang, Jiang, Dudley (bib0028) 2017 Esfandiari, Babavalian, Moghadam, Tabar (bib0009) 2014; 41 Exarchos, Papaloukas, Fotiadis, Michalis (bib0077) 2006; 53 Kurgan, Musilek (bib0015) 2006; 21 Ma, Guo (bib0123) 2014 Ling, Sheng (bib0093) 2008 A. Kofod-petersen, “How to do a Structured Literature Review in computer science.” pp. 1–7, 2014. Qin, Guan, Wang (bib0020) Dec. 2017; 29 Seah, Tang, Kitchen, Gaillard, Dixon (bib0041) 2019 Yu, Liu (bib0021) 2004; 5 Jovic, Brkic, Bogunovic (bib0019) May 2015 Huang, Chan, Dong (bib0063) 2017; 66 Jaganathan, Kuppuchamy (bib0054) 2013; 43 Idri, Benhar, Fernández-Alemán, Kadi (bib0026) Aug. 2018; 162 López, Fernández, García, Palade, Herrera (bib0096) Nov. 2013; 250 Nguyen, Bouzerdoum, Phung (bib0092) 2009 Abdar, Książek, Acharya, Tan, Makarenkov, Pławiak (bib0091) 2019 Kitchenham, Charters (bib0045) 2007; 45 Benhar, Idri, Fernández-Alemán (bib0011) Jan. 2019; 43 “UCI Machine Learning Repository: Heart Disease Data Set,” 2019. Maimon, Rokach (bib0121) 2010 Son, Kim, Kim, Park, Kim (bib0112) 2012; 45 Quinlan (bib0127) 1986; 1 Sáez, Krawczyk, Woźniak (bib0033) 2016; 30 Idri, Abnane, Abran (bib0053) 2015 Ragothaman, Sarojini (bib0013) Dec. 2016; 9 The CORE Conference Ranking Exercise – CORE Portal. Sufi, Khalil (bib0079) 2011; 15 Zhu, Zhu, Chen (bib0106) 2005 Hannun (bib0038) 2019 Zhang, Kambhampati, Davis, Goode, Cleland (bib0062) 2012 Verma, Srivastava, Negi (bib0058) 2017 Gaziano (bib0001) 2007 Idri, Abnane, Abran (bib0059) 2016; 117 Peter, Somasundaram (bib0056) 2012; 2 Crone (10.1016/j.cmpb.2020.105635_bib0078) 2006 Subasi (10.1016/j.cmpb.2020.105635_bib0104) 2010; 37 Haixiang (10.1016/j.cmpb.2020.105635_bib0017) 2017; 73 Vivekanandan (10.1016/j.cmpb.2020.105635_bib0114) 2017; 90 Idri (10.1016/j.cmpb.2020.105635_bib0059) 2016; 117 Sardi (10.1016/j.cmpb.2020.105635_bib0051) 2017; 71 Ling (10.1016/j.cmpb.2020.105635_bib0093) 2008 Bhargava (10.1016/j.cmpb.2020.105635_bib0126) 2013; 3 Kadi (10.1016/j.cmpb.2020.105635_bib0006) 2017; 1 Idri (10.1016/j.cmpb.2020.105635_bib0050) 2016; 118 Tuncer (10.1016/j.cmpb.2020.105635_bib0085) 2019 Banerjee (10.1016/j.cmpb.2020.105635_bib0064) 2018 Asl (10.1016/j.cmpb.2020.105635_bib0072) 2008; 44 Gao (10.1016/j.cmpb.2020.105635_bib0107) 2012 Verma (10.1016/j.cmpb.2020.105635_bib0058) 2017 Aziz (10.1016/j.cmpb.2020.105635_bib0071) 2017 Shao (10.1016/j.cmpb.2020.105635_bib0003) 2014; 14 Ragothaman (10.1016/j.cmpb.2020.105635_bib0013) 2016; 9 Al Khaldy (10.1016/j.cmpb.2020.105635_bib0095) 2018 Al Shalabi (10.1016/j.cmpb.2020.105635_bib0080) 2006; 2 Poolsawad (10.1016/j.cmpb.2020.105635_bib0022) 2014; 11 Idri (10.1016/j.cmpb.2020.105635_bib0053) 2015 Idri (10.1016/j.cmpb.2020.105635_bib0052) 2015; 58 Peter (10.1016/j.cmpb.2020.105635_bib0056) 2012; 2 Fayyad (10.1016/j.cmpb.2020.105635_bib0014) 1996; 17 Abawajy (10.1016/j.cmpb.2020.105635_bib0061) 2013; 112 Kolukisa (10.1016/j.cmpb.2020.105635_bib0066) 2019 Zhang (10.1016/j.cmpb.2020.105635_bib0062) 2012 López (10.1016/j.cmpb.2020.105635_bib0096) 2013; 250 Mustaqeem (10.1016/j.cmpb.2020.105635_bib0005) 2017 Jaganathan (10.1016/j.cmpb.2020.105635_bib0054) 2013; 43 Idri (10.1016/j.cmpb.2020.105635_bib0008) 2018 Tang (10.1016/j.cmpb.2020.105635_bib0108) 2004 Zreik (10.1016/j.cmpb.2020.105635_bib0043) 2018 Xu (10.1016/j.cmpb.2020.105635_bib0105) 2006; 2 Quinlan (10.1016/j.cmpb.2020.105635_bib0127) 1986; 1 Zhou (10.1016/j.cmpb.2020.105635_bib0082) 2015; 26 10.1016/j.cmpb.2020.105635_bib0049 10.1016/j.cmpb.2020.105635_bib0048 10.1016/j.cmpb.2020.105635_bib0046 García (10.1016/j.cmpb.2020.105635_bib0012) 2015 Jabbar (10.1016/j.cmpb.2020.105635_bib0031) 2015 Hannun (10.1016/j.cmpb.2020.105635_bib0038) 2019 Benhar (10.1016/j.cmpb.2020.105635_bib0011) 2019; 43 Jerez (10.1016/j.cmpb.2020.105635_bib0060) 2010; 50 Zhu (10.1016/j.cmpb.2020.105635_bib0106) 2005 Karabulut (10.1016/j.cmpb.2020.105635_bib0102) 2012; 1 Wang (10.1016/j.cmpb.2020.105635_bib0035) 2013; 116 Son (10.1016/j.cmpb.2020.105635_bib0112) 2012; 45 Bachri (10.1016/j.cmpb.2020.105635_bib0100) 2017; 8 Jabbar (10.1016/j.cmpb.2020.105635_bib0083) 2013 Guyon (10.1016/j.cmpb.2020.105635_bib0018) 2006; 207 El Idrissi (10.1016/j.cmpb.2020.105635_bib0007) 2018 Yu (10.1016/j.cmpb.2020.105635_bib0084) 2009; 46 Sáez (10.1016/j.cmpb.2020.105635_bib0033) 2016; 30 Petersen (10.1016/j.cmpb.2020.105635_bib0047) 2015; 64 Zadeh (10.1016/j.cmpb.2020.105635_bib0094) 1999; 46 Vivencio (10.1016/j.cmpb.2020.105635_bib0097) 2007 Rajesh (10.1016/j.cmpb.2020.105635_bib0116) 2018 Abdel-Aal (10.1016/j.cmpb.2020.105635_bib0110) 2005; 80 MTW (10.1016/j.cmpb.2020.105635_bib0025) 1999 Sufi (10.1016/j.cmpb.2020.105635_bib0079) 2011; 15 Loh (10.1016/j.cmpb.2020.105635_bib0027) 2017 Qin (10.1016/j.cmpb.2020.105635_bib0020) 2017; 29 Jiang (10.1016/j.cmpb.2020.105635_bib0037) 2006 Idri (10.1016/j.cmpb.2020.105635_bib0026) 2018; 162 Huang (10.1016/j.cmpb.2020.105635_bib0063) 2017; 66 Sasikala (10.1016/j.cmpb.2020.105635_bib0034) 2014; 243 10.1016/j.cmpb.2020.105635_bib0101 Cover (10.1016/j.cmpb.2020.105635_bib0125) 1967; 13 Benhar (10.1016/j.cmpb.2020.105635_bib0024) 2020 Jabbar (10.1016/j.cmpb.2020.105635_bib0055) 2013; 13 Yu (10.1016/j.cmpb.2020.105635_bib0021) 2004; 5 Law (10.1016/j.cmpb.2020.105635_bib0120) 2000; 21 Ma (10.1016/j.cmpb.2020.105635_bib0123) 2014 Betancur (10.1016/j.cmpb.2020.105635_bib0042) 2018 Miotto (10.1016/j.cmpb.2020.105635_bib0028) 2017 Bolón-Canedo (10.1016/j.cmpb.2020.105635_bib0074) 2015; 86 Anbarasi (10.1016/j.cmpb.2020.105635_bib0069) 2010 Han (10.1016/j.cmpb.2020.105635_bib0124) 2012 Abdar (10.1016/j.cmpb.2020.105635_bib0091) 2019 Li (10.1016/j.cmpb.2020.105635_bib0115) 2002 Dreiseitl (10.1016/j.cmpb.2020.105635_bib0098) 2002; 35 Abraham (10.1016/j.cmpb.2020.105635_bib0076) 2007 Kohli (10.1016/j.cmpb.2020.105635_bib0118) 2018 Yilmaz (10.1016/j.cmpb.2020.105635_bib0002) 2014; 38 Melgani (10.1016/j.cmpb.2020.105635_bib0067) 2008; 12 Jovic (10.1016/j.cmpb.2020.105635_bib0019) 2015 Pławiak (10.1016/j.cmpb.2020.105635_bib0086) 2019 Pizzi (10.1016/j.cmpb.2020.105635_bib0065) 2011; 412 Madani (10.1016/j.cmpb.2020.105635_bib0040) 2018 Kutlu (10.1016/j.cmpb.2020.105635_bib0081) 2011; 41 Gaziano (10.1016/j.cmpb.2020.105635_bib0001) 2007 Esfandiari (10.1016/j.cmpb.2020.105635_bib0009) 2014; 41 Nguyen (10.1016/j.cmpb.2020.105635_bib0092) 2009 Song (10.1016/j.cmpb.2020.105635_bib0032) 2005; 3 Seah (10.1016/j.cmpb.2020.105635_bib0041) 2019 Zaki (10.1016/j.cmpb.2020.105635_bib0075) 2014 Polat (10.1016/j.cmpb.2020.105635_bib0113) 2009; 36 10.1016/j.cmpb.2020.105635_bib0004 Kitchenham (10.1016/j.cmpb.2020.105635_bib0045) 2007; 45 Gardner (10.1016/j.cmpb.2020.105635_bib0119) 1998; 32 García (10.1016/j.cmpb.2020.105635_bib0070) 2015; 72 Amato (10.1016/j.cmpb.2020.105635_bib0099) 2013; 11 Maimon (10.1016/j.cmpb.2020.105635_bib0121) 2010 Hejazi (10.1016/j.cmpb.2020.105635_bib0073) 2015; 29 Janardhanan (10.1016/j.cmpb.2020.105635_bib0103) 2015; 11 Fitkov-Norris (10.1016/j.cmpb.2020.105635_bib0090) 2012 Babaoglu (10.1016/j.cmpb.2020.105635_bib0111) 2010; 37 Vapnik (10.1016/j.cmpb.2020.105635_bib0122) 2000 Kurgan (10.1016/j.cmpb.2020.105635_bib0015) 2006; 21 Krittanawong (10.1016/j.cmpb.2020.105635_bib0029) 2019 Lecun (10.1016/j.cmpb.2020.105635_bib0030) 2015 10.1016/j.cmpb.2020.105635_bib0089 10.1016/j.cmpb.2020.105635_bib0088 10.1016/j.cmpb.2020.105635_bib0087 Patil (10.1016/j.cmpb.2020.105635_bib0023) 2010; 94 Alickovic (10.1016/j.cmpb.2020.105635_bib0036) 2014; 34 Liu (10.1016/j.cmpb.2020.105635_bib0044) 2018; 1087 Pławiak (10.1016/j.cmpb.2020.105635_bib0068) 2018 Kadi (10.1016/j.cmpb.2020.105635_bib0010) 2017; 97 Amin (10.1016/j.cmpb.2020.105635_bib0117) 2019 Dag (10.1016/j.cmpb.2020.105635_bib0016) 2017; 94 Exarchos (10.1016/j.cmpb.2020.105635_bib0077) 2006; 53 Tison (10.1016/j.cmpb.2020.105635_bib0039) 2018 Almuhaideb (10.1016/j.cmpb.2020.105635_bib0057) 2016; 10 Patil (10.1016/j.cmpb.2020.105635_bib0109) 2010; 37 |
| References_xml | – start-page: 373 year: 2004 end-page: 378 ident: bib0108 article-title: Noise identification with the k-means algorithm publication-title: 16th IEEE Int. Conf. Tools with Artif. Intell. – volume: 1087 year: Sep. 2018 ident: bib0044 article-title: Feature Extraction and Image Recognition with Convolutional Neural Networks publication-title: J. Phys. Conf. Ser. – reference: “Statlog (Heart) Data Set.” – year: 2008 ident: bib0093 article-title: Cost-Sensitive Learning and the Class Imbalance Problem publication-title: Encycl. Mach. Learn. – year: 2018 ident: bib0118 article-title: Application of machine learning in disease prediction publication-title: 2018 4th International Conference on Computing Communication and Automation, ICCCA 2018 – volume: 73 start-page: 220 year: 2017 end-page: 239 ident: bib0017 article-title: Learning from class-imbalanced data: Review of methods and applications publication-title: Expert Systems with Applications – year: 2014 ident: bib0123 article-title: Support Vector Machines Applications – start-page: 481 year: 2007 end-page: 485 ident: bib0097 article-title: Feature-weighted k-nearest neighbor classifier publication-title: Proceedings of the 2007 IEEE Symposium on Foundations of Computational Intelligence, FOCI 2007 – year: 2017 ident: bib0028 article-title: Deep learning for healthcare: Review, opportunities and challenges publication-title: Brief. Bioinform. – year: 2018 ident: bib0064 article-title: Time series and morphological feature extraction for classifying coronary artery disease from photoplethysmogram publication-title: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings – year: 2012 ident: bib0090 article-title: Evaluating the Impact of Categorical Data Encoding and Scaling on Neural Network Classification Performance: The Case of Repeat Consumption of Identical Cultural Goods publication-title: Communications in Computer and Information Science – volume: 58 start-page: 206 year: 2015 end-page: 230 ident: bib0052 article-title: Analogy-based software development effort estimation: A systematic mapping and review publication-title: Information and Software Technology – volume: 26 start-page: S1757 year: 2015 end-page: S1762 ident: bib0082 article-title: Fuzzy Naive Bayesian for constructing regulated network with weights publication-title: Biomed. Mater. Eng. – start-page: 592 year: 2005 end-page: 596 ident: bib0106 article-title: Effect Analysis of Dimension Reduction on Support Vector Machines publication-title: 2005 International Conference on Natural Language Processing and Knowledge Engineering – start-page: 1 year: 2006 end-page: 4 ident: bib0037 article-title: ECG Arrhythmias Recognition System Based on Independent Component Analysis Feature Extraction publication-title: TENCON 2006 - 2006 IEEE Region 10 Conference – start-page: 44 year: Dec. 2007 end-page: 49 ident: bib0076 article-title: Medical Datamining with a New Algorithm for Feature Selection and Naive Bayesian Classifier publication-title: 10th International Conference on Information Technology (ICIT 2007) – start-page: 391 year: 2020 end-page: 398 ident: bib0024 article-title: Impact of Threshold Values for Filter-based Univariate Feature Selection in Heart Disease Classification publication-title: Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies – start-page: 1 year: Mar. 2015 end-page: 6 ident: bib0031 article-title: Computational intelligence technique for early diagnosis of heart disease publication-title: 2015 IEEE International Conference on Engineering and Technology (ICETECH) – reference: . (accessed Nov. 26, 2019). – volume: 38 year: 2014 ident: bib0002 article-title: A New Data Preparation Method Based on Clustering Algorithms for Diagnosis Systems of Heart and Diabetes Diseases publication-title: J. Med. Syst. – year: 2019 ident: bib0086 article-title: Novel deep genetic ensemble of classifiers for arrhythmia detection using ECG signals publication-title: Neural Comput. Appl. – volume: 1 start-page: 81 year: 1986 end-page: 106 ident: bib0127 article-title: Induction of Decision Trees publication-title: Mach. Learn. – start-page: 657 year: 2002 end-page: 660 ident: bib0115 article-title: Improving medical/biological data classification performance by wavelet preprocessing publication-title: 2002 IEEE International Conference on Data Mining, 2002. Proceedings. – reference: “No Title, ” Arrhythmia Data Set. – volume: 250 start-page: 113 year: Nov. 2013 end-page: 141 ident: bib0096 article-title: An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics publication-title: Inf. Sci. (Ny). – year: 2018 ident: bib0116 article-title: Classification of imbalanced ECG beats using re-sampling techniques and AdaBoost ensemble classifier publication-title: Biomed. Signal Process. Control – volume: 1 year: Aug. 2017 ident: bib0006 article-title: Systematic mapping study of data mining–based empirical studies in cardiology publication-title: Health Informatics J – volume: 112 start-page: 720 year: 2013 end-page: 730 ident: bib0061 article-title: Multistage approach for clustering and classification of ECG data publication-title: Comput. Methods Programs Biomed. – year: 2018 ident: bib0095 article-title: Performance Analysis of Various Missing Value Imputation Methods on Heart Failure Dataset publication-title: Lecture Notes in Networks and Systems – year: 2019 ident: bib0038 article-title: Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network publication-title: Nature Medicine – volume: 117 start-page: 595 year: 2016 end-page: 611 ident: bib0059 article-title: Missing data techniques in analogy-based software development effort estimation publication-title: J. Syst. Softw. – volume: 5 start-page: 1205 year: 2004 end-page: 1224 ident: bib0021 article-title: Efficient Feature Selection via Analysis of Relevance and Redundancy publication-title: J. Mach. Learn. Res. – volume: 94 start-page: 423 year: 2010 end-page: 434 ident: bib0023 article-title: Impact of K-means on the performance of classifiers for labeled data publication-title: Communications in Computer and Information Science – volume: 41 start-page: 37 year: 2011 end-page: 45 ident: bib0081 article-title: A multi-stage automatic arrhythmia recognition and classification system publication-title: Comput. Biol. Med. – reference: J. Mohamad-Saleh and B. S. Hoyle, “Improved Neural Network Performance Using Principal Component Analysis on Matlab.” 2008. – volume: 30 start-page: 590 year: 2016 end-page: 609 ident: bib0033 article-title: On the Influence of Class Noise in Medical Data Classification: Treatment Using Noise Filtering Methods publication-title: Appl. Artif. Intell. – volume: 50 start-page: 105 year: 2010 end-page: 115 ident: bib0060 article-title: Missing data imputation using statistical and machine learning methods in a real breast cancer problem publication-title: Artif. Intell. Med. – year: 2019 ident: bib0041 article-title: Chest radiographs in congestive heart failure: Visualizing neural network learning publication-title: Radiology – year: 2006 ident: bib0078 article-title: The impact of preprocessing on data mining: An evaluation of classifier sensitivity in direct marketing publication-title: Eur. J. Oper. Res. – volume: 66 start-page: 161 year: 2017 end-page: 170 ident: bib0063 article-title: MACE prediction of acute coronary syndrome via boosted resampling classification using electronic medical records publication-title: J. Biomed. Inform. – volume: 118 start-page: 151 year: 2016 end-page: 175 ident: bib0050 article-title: Systematic literature review of ensemble effort estimation publication-title: J. Syst. Softw. – year: 2000 ident: bib0122 article-title: The Nature of Statistical Learning Theory – volume: 37 start-page: 8659 year: 2010 end-page: 8666 ident: bib0104 article-title: EEG signal classification using PCA, ICA, LDA and support vector machines publication-title: Expert Syst. Appl. – volume: 94 start-page: 42 year: 2017 end-page: 52 ident: bib0016 article-title: Predicting heart transplantation outcomes through data analytics publication-title: Decis. Support Syst. – year: 1999 ident: bib0025 article-title: Feature Extraction Construction and Selection: A Data Mining Perspective publication-title: J. Am. Stat. Assoc. – volume: 11 start-page: 162 year: 2014 end-page: 179 ident: bib0022 article-title: Issues in the mining of heart failure datasets publication-title: Int. J. Autom. Comput. – volume: 116 start-page: 38 year: Sep. 2013 end-page: 45 ident: bib0035 article-title: ECG arrhythmia classification using a probabilistic neural network with a feature reduction method publication-title: Neurocomputing – volume: 13 start-page: 5 year: 2013 end-page: 14 ident: bib0055 article-title: Classification of Heart Disease using Artificial Neural Network and Feature Subset Selection publication-title: Glob. J. Comput. Sci. Technol. – start-page: 1200 year: May 2015 end-page: 1205 ident: bib0019 article-title: A review of feature selection methods with applications publication-title: 2015 38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO) – reference: “UCI Machine Learning Repository: Heart Disease Data Set,” 2019. – year: 2018 ident: bib0042 article-title: Deep Learning for Prediction of Obstructive Disease From Fast Myocardial Perfusion SPECT: A Multicenter Study publication-title: JACC Cardiovasc. Imaging – year: 2019 ident: bib0091 article-title: A new machine learning technique for an accurate diagnosis of coronary artery disease publication-title: Comput. Methods Programs Biomed. – start-page: 1142 year: 2018 end-page: 1152 ident: bib0007 article-title: Data Mining Techniques in Diabetes Self-management: A Systematic Map publication-title: Trends and Advances in Information Systems and Technologies – volume: 8 start-page: 731 year: 2017 end-page: 739 ident: bib0100 article-title: Feature selection based on CHI square in artificial neural network to predict the accuracy of student study period publication-title: Int. J. Civ. Eng. Technol. – year: 2019 ident: bib0085 article-title: Automated arrhythmia detection using novel hexadecimal local pattern and multilevel wavelet transform with ECG signals publication-title: Knowledge-Based Syst – volume: 43 start-page: 17 year: Jan. 2019 ident: bib0011 article-title: A Systematic Mapping Study of Data Preparation in Heart Disease Knowledge Discovery publication-title: J. Med. Syst. – year: 2010 ident: bib0069 article-title: Enhanced Prediction of Heart Disease with Feature Subset Selection using Genetic Algorithm publication-title: Int. J. Eng. Sci. Technol. – reference: “World Health Organization,” 2019. – volume: 29 year: Dec. 2017 ident: bib0020 article-title: APPLICATION OF ENSEMBLE ALGORITHM INTEGRATING MULTIPLE CRITERIA FEATURE SELECTION IN CORONARY HEART DISEASE DETECTION publication-title: Biomed. Eng. Appl. Basis Commun. – volume: 32 start-page: 2627 year: Aug. 1998 end-page: 2636 ident: bib0119 article-title: Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences publication-title: Atmos. Environ. – year: 2014 ident: bib0075 article-title: Data Mining and Analysis: Fundamental Concepts and Algorithms – year: 2017 ident: bib0058 article-title: An intelligent noninvasive model for coronary artery disease detection publication-title: Complex Intell. Syst. – start-page: 1 year: 2018 end-page: 10 ident: bib0008 article-title: A systematic map of data analytics in breast cancer publication-title: Proceedings of the Australasian Computer Science Week Multiconference on - ACSW ’18 – volume: 10 start-page: 1082 year: 2016 end-page: 1102 ident: bib0057 article-title: Impact of preprocessing on medical data classification publication-title: Front. Comput. Sci. – start-page: 3656 year: 2017 end-page: 3659 ident: bib0005 article-title: Wrapper method for feature selection to classify cardiac arrhythmia publication-title: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society – volume: 64 start-page: 1 year: 2015 end-page: 18 ident: bib0047 article-title: Guidelines for conducting systematic mapping studies in software engineering: An update publication-title: Information and Software Technology – volume: 43 start-page: 2222 year: 2013 end-page: 2229 ident: bib0054 article-title: A threshold fuzzy entropy based feature selection for medical database classification publication-title: Comput. Biol. Med. – volume: 412 start-page: 5909 year: 2011 end-page: 5925 ident: bib0065 article-title: Fuzzy quartile encoding as a preprocessing method for biomedical pattern classification publication-title: Theor. Comput. Sci. – volume: 162 start-page: 69 year: Aug. 2018 end-page: 85 ident: bib0026 article-title: A systematic map of medical data preprocessing in knowledge discovery publication-title: Comput. Methods Programs Biomed. – volume: 2 start-page: 735 year: 2006 end-page: 739 ident: bib0080 article-title: Data Mining: A Preprocessing Engine publication-title: J. Comput. Sci. – volume: 90 start-page: 125 year: 2017 end-page: 136 ident: bib0114 article-title: Optimal feature selection using a modified differential evolution algorithm and its effectiveness for prediction of heart disease publication-title: Comput. Biol. Med. – year: 2012 ident: bib0124 article-title: Data Mining: Concepts and Techniques – volume: 243 start-page: 599 year: 2014 end-page: 608 ident: bib0034 article-title: RF-SEA-Based Feature Selection for Data Classification in Medical Domain publication-title: Intell. Comput. Networking, Informatics – volume: 86 start-page: 33 year: Sep. 2015 end-page: 45 ident: bib0074 article-title: Recent advances and emerging challenges of feature selection in the context of big data publication-title: Knowledge-Based Syst – volume: 14 start-page: 47 year: 2014 end-page: 52 ident: bib0003 article-title: Hybrid intelligent modeling schemes for heart disease classification publication-title: Appl. Soft Comput. J. – volume: 17 start-page: 37 year: 1996 ident: bib0014 article-title: From Data Mining to Knowledge Discovery in Databases publication-title: AI Mag – year: 2018 ident: bib0068 article-title: Novel methodology of cardiac health recognition based on ECG signals and evolutionary-neural system publication-title: Expert Syst. Appl. – volume: 37 start-page: 3177 year: 2010 end-page: 3183 ident: bib0111 article-title: A comparison of feature selection models utilizing binary particle swarm optimization and genetic algorithm in determining coronary artery disease using support vector machine publication-title: Expert Syst. Appl. – reference: “The CORE Conference Ranking Exercise – CORE Portal.” – volume: 71 start-page: 31 year: Jul. 2017 end-page: 48 ident: bib0051 article-title: A systematic review of gamification in e-Health publication-title: J. Biomed. Inform. – volume: 37 start-page: 8102 year: 2010 end-page: 8108 ident: bib0109 article-title: Hybrid prediction model for Type-2 diabetic patients publication-title: Expert Syst. Appl. – volume: 45 start-page: 999 year: 2012 end-page: 1008 ident: bib0112 article-title: Decision-making model for early diagnosis of congestive heart failure using rough set and decision tree approaches publication-title: J. Biomed. Inform. – volume: 2 year: 2006 ident: bib0105 article-title: Adaptive Intrusion Detection Based on Machine Learning: Feature Extraction, Classifier Construction and Sequential Pattern Prediction publication-title: Int. J. Web Serv. Pract. – volume: 45 start-page: 1051 year: 2007 ident: bib0045 article-title: Guidelines for performing Systematic Literature reviews in Software Engineering Version 2.3 publication-title: Engineering – year: 2015 ident: bib0012 article-title: Tutorial on practical tips of the most influential data preprocessing algorithms in data mining publication-title: Knowledge-Based Systems – start-page: 1 year: 2015 end-page: 8 ident: bib0053 article-title: Systematic Mapping Study of Missing Values Techniques in Software Engineering Data publication-title: International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), 2015 16th IEEE/ACIS – volume: 12 start-page: 667 year: 2008 end-page: 677 ident: bib0067 article-title: Classification of electrocardiogram signals with support vector machines and particle swarm optimization publication-title: IEEE Trans. Inf. Technol. Biomed. – volume: 1 start-page: 323 year: 2012 end-page: 327 ident: bib0102 article-title: A comparative study on the effect of feature selection on classification accuracy publication-title: Procedia Technol – volume: 80 start-page: 141 year: 2005 end-page: 153 ident: bib0110 article-title: Improved classification of medical data using abductive network committees trained on different feature subsets publication-title: Comput. Methods Programs Biomed. – volume: 72 year: 2015 ident: bib0070 publication-title: Data Preprocessing in Data Mining – volume: 36 start-page: 10367 year: 2009 end-page: 10373 ident: bib0113 article-title: A new feature selection method on classification of medical datasets: Kernel F-score feature selection publication-title: Expert Syst. Appl. – year: 2018 ident: bib0043 article-title: Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis publication-title: Med. Image Anal. – volume: 3 start-page: 1114 year: 2013 end-page: 1119 ident: bib0126 article-title: Decision Tree Analysis on J48 Algorithm for Data Mining publication-title: Int. J. Adv. Res. Comput. Sci. Softw. Eng. – year: 2007 ident: bib0001 article-title: Reducing the growing burden of cardiovascular disease in the developing world publication-title: Health Affairs – reference: A. Kofod-petersen, “How to do a Structured Literature Review in computer science.” pp. 1–7, 2014. – reference: rankings (accessed Dec. 02, 2019). – year: 2017 ident: bib0027 article-title: Deep learning for cardiac computer-aided diagnosis: benefits, issues & solutions publication-title: mHealth – volume: 13 start-page: 21 year: Jan. 1967 end-page: 27 ident: bib0125 article-title: Nearest neighbor pattern classification publication-title: IEEE Trans. Inf. Theory – year: 2019 ident: bib0117 article-title: Identification of significant features and data mining techniques in predicting heart disease publication-title: Telemat. Informatics – volume: 46 start-page: 165 year: 2009 end-page: 178 ident: bib0084 article-title: Noise-tolerant electrocardiogram beat classification based on higher order statistics of subband components publication-title: Artif. Intell. Med. – volume: 11 start-page: 47 year: 2013 end-page: 58 ident: bib0099 article-title: Artificial neural networks in medical diagnosis publication-title: Journal of Applied Biomedicine – start-page: 1984 year: Jul. 2012 end-page: 1988 ident: bib0107 article-title: Fault detection and diagnosis for spacecraft using principal component analysis and support vector machines publication-title: 2012 7th IEEE Conference on Industrial Electronics and Applications (ICIEA) – start-page: 47 year: Dec. 2013 end-page: 54 ident: bib0083 article-title: Heart disease classification using nearest neighbor classifier with feature subset selection publication-title: Ann. .COMPUTER Sci. Ser. – year: 2019 ident: bib0029 article-title: Deep learning for cardiovascularmedicine: A practical primer publication-title: European Heart Journal – volume: 29 start-page: 660 year: 2015 end-page: 674 ident: bib0073 article-title: Multiclass Support Vector Machines for Classification of ECG Data with Missing Values publication-title: Appl. Artif. Intell. – volume: 15 start-page: 33 year: 2011 end-page: 39 ident: bib0079 article-title: Diagnosis of cardiovascular abnormalities from compressed ECG: A data mining-based approach publication-title: IEEE Trans. Inf. Technol. Biomed. – start-page: 193 year: 2009 end-page: 208 ident: bib0092 article-title: Learning pattern classification tasks with imbalanced data sets publication-title: Pattern Recognit – volume: 41 start-page: 4434 year: 2014 end-page: 4463 ident: bib0009 article-title: Knowledge discovery in medicine: Current issue and future trend publication-title: Expert Systems with Applications – volume: 46 start-page: 105 year: 1999 end-page: 119 ident: bib0094 article-title: From computing with numbers to computing with words - From manipulation of measurements to manipulation of perceptions publication-title: IEEE Trans. Circuits Syst. I Fundam. Theory Appl. – volume: 44 start-page: 51 year: 2008 end-page: 64 ident: bib0072 article-title: Support vector machine-based arrhythmia classification using reduced features of heart rate variability signal publication-title: Artif. Intell. Med. – volume: 21 start-page: 331 year: Aug. 2000 end-page: 340 ident: bib0120 article-title: Back-propagation learning in improving the accuracy of neural network-based tourism demand forecasting publication-title: Tour. Manag. – volume: 97 start-page: 12 year: 2017 end-page: 32 ident: bib0010 article-title: Knowledge discovery in cardiology: A systematic literature review publication-title: Int. J. Med. Inform. – year: 2015 ident: bib0030 article-title: Deep learning publication-title: Nature – volume: 3 start-page: 571 year: 2005 end-page: 579 ident: bib0032 article-title: Support vector machine-based arrhythmia classification using reduced features publication-title: Int. J. Control. Autom. Syst. – volume: 9 year: Dec. 2016 ident: bib0013 article-title: A Multi-objective Non-Dominated Sorted Artificial Bee Colony Feature Selection Algorithm for Medical Datasets publication-title: Indian J. Sci. Technol. – volume: 21 start-page: 1 year: 2006 ident: bib0015 article-title: A survey of Knowledge Discovery and Data Mining process models publication-title: Knowl. Eng. Rev. – year: 2018 ident: bib0039 article-title: Passive detection of atrial fibrillation using a commercially available smartwatch publication-title: JAMA Cardiol – reference: “Journal Citation Reports.” – volume: 35 start-page: 352 year: Oct. 2002 end-page: 359 ident: bib0098 article-title: Logistic regression and artificial neural network classification models: a methodology review publication-title: J. Biomed. Inform. – volume: 2 start-page: 1 year: 2012 end-page: 7 ident: bib0056 article-title: Study and Development of Novel Feature Selection Framework for Heart Disease Prediction publication-title: Int. J. Sci. Res. Publ. – volume: 11 start-page: 25 year: . 2015 ident: bib0103 article-title: Effectiveness of Support Vector Machines in Medical Data mining publication-title: J. Commun. Softw. Syst. – year: 2010 ident: bib0121 article-title: Data Mining and Knowledge Discovery Handbook – year: 2018 ident: bib0040 article-title: Fast and accurate view classification of echocardiograms using deep learning publication-title: npj Digit. Med. – reference: . – volume: 207 year: 2006 ident: bib0018 publication-title: Feature Extraction: Foundations and Applications – year: 2017 ident: bib0071 article-title: Dimension reduction methods for microarray data: a review publication-title: AIMS Bioeng – start-page: 2840 year: 2012 end-page: 2844 ident: bib0062 article-title: A comparative study of missing value imputation with multiclass classification for clinical heart failure data publication-title: Proceedings - 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2012 – year: 2019 ident: bib0066 article-title: Evaluation of Classification Algorithms, Linear Discriminant Analysis and a New Hybrid Feature Selection Methodology for the Diagnosis of Coronary Artery Disease publication-title: Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018 – volume: 34 start-page: 513 year: 2014 end-page: 533 ident: bib0036 article-title: Effect of Multiscale PCA De-noising in ECG Beat Classification for Diagnosis of Cardiovascular Diseases publication-title: Circuits, Syst. Signal Process. – reference: (accessed Oct. 08, 2019). – volume: 53 start-page: 1531 year: 2006 end-page: 1540 ident: bib0077 article-title: An association rule mining-based methodology for automated detection of ischemic ECG beats publication-title: IEEE Trans. Biomed. Eng. – year: 2007 ident: 10.1016/j.cmpb.2020.105635_bib0001 article-title: Reducing the growing burden of cardiovascular disease in the developing world publication-title: Health Affairs doi: 10.1377/hlthaff.26.1.13 – start-page: 47 year: 2013 ident: 10.1016/j.cmpb.2020.105635_bib0083 article-title: Heart disease classification using nearest neighbor classifier with feature subset selection publication-title: Ann. .COMPUTER Sci. Ser. – start-page: 1 year: 2015 ident: 10.1016/j.cmpb.2020.105635_bib0053 article-title: Systematic Mapping Study of Missing Values Techniques in Software Engineering Data – volume: 17 start-page: 37 issue: 3 year: 1996 ident: 10.1016/j.cmpb.2020.105635_bib0014 article-title: From Data Mining to Knowledge Discovery in Databases publication-title: AI Mag – ident: 10.1016/j.cmpb.2020.105635_bib0046 – year: 2015 ident: 10.1016/j.cmpb.2020.105635_bib0030 article-title: Deep learning publication-title: Nature doi: 10.1038/nature14539 – start-page: 657 year: 2002 ident: 10.1016/j.cmpb.2020.105635_bib0115 article-title: Improving medical/biological data classification performance by wavelet preprocessing – volume: 14 start-page: 47 issue: PART A year: 2014 ident: 10.1016/j.cmpb.2020.105635_bib0003 article-title: Hybrid intelligent modeling schemes for heart disease classification publication-title: Appl. Soft Comput. J. doi: 10.1016/j.asoc.2013.09.020 – volume: 13 start-page: 21 issue: 1 year: 1967 ident: 10.1016/j.cmpb.2020.105635_bib0125 article-title: Nearest neighbor pattern classification publication-title: IEEE Trans. Inf. Theory doi: 10.1109/TIT.1967.1053964 – volume: 1087 year: 2018 ident: 10.1016/j.cmpb.2020.105635_bib0044 article-title: Feature Extraction and Image Recognition with Convolutional Neural Networks publication-title: J. Phys. Conf. Ser. doi: 10.1088/1742-6596/1087/6/062032 – ident: 10.1016/j.cmpb.2020.105635_bib0089 – volume: 1 start-page: 81 issue: 1 year: 1986 ident: 10.1016/j.cmpb.2020.105635_bib0127 article-title: Induction of Decision Trees publication-title: Mach. Learn. doi: 10.1007/BF00116251 – volume: 94 start-page: 42 year: 2017 ident: 10.1016/j.cmpb.2020.105635_bib0016 article-title: Predicting heart transplantation outcomes through data analytics publication-title: Decis. Support Syst. doi: 10.1016/j.dss.2016.10.005 – year: 2019 ident: 10.1016/j.cmpb.2020.105635_bib0085 article-title: Automated arrhythmia detection using novel hexadecimal local pattern and multilevel wavelet transform with ECG signals publication-title: Knowledge-Based Syst doi: 10.1016/j.knosys.2019.104923 – year: 2012 ident: 10.1016/j.cmpb.2020.105635_bib0124 – volume: 41 start-page: 37 issue: 1 year: 2011 ident: 10.1016/j.cmpb.2020.105635_bib0081 article-title: A multi-stage automatic arrhythmia recognition and classification system publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2010.11.003 – year: 2014 ident: 10.1016/j.cmpb.2020.105635_bib0075 – year: 2019 ident: 10.1016/j.cmpb.2020.105635_bib0029 article-title: Deep learning for cardiovascularmedicine: A practical primer publication-title: European Heart Journal doi: 10.1093/eurheartj/ehz056 – volume: 26 start-page: S1757 issue: s1 year: 2015 ident: 10.1016/j.cmpb.2020.105635_bib0082 article-title: Fuzzy Naive Bayesian for constructing regulated network with weights publication-title: Biomed. Mater. Eng. – volume: 2 start-page: 735 issue: 9 year: 2006 ident: 10.1016/j.cmpb.2020.105635_bib0080 article-title: Data Mining: A Preprocessing Engine publication-title: J. Comput. Sci. doi: 10.3844/jcssp.2006.735.739 – volume: 13 start-page: 5 issue: 3 year: 2013 ident: 10.1016/j.cmpb.2020.105635_bib0055 article-title: Classification of Heart Disease using Artificial Neural Network and Feature Subset Selection publication-title: Glob. J. Comput. Sci. Technol. – year: 2017 ident: 10.1016/j.cmpb.2020.105635_bib0058 article-title: An intelligent noninvasive model for coronary artery disease detection publication-title: Complex Intell. Syst. – year: 2000 ident: 10.1016/j.cmpb.2020.105635_bib0122 – volume: 94 start-page: 423 issue: PART 1 year: 2010 ident: 10.1016/j.cmpb.2020.105635_bib0023 article-title: Impact of K-means on the performance of classifiers for labeled data publication-title: Communications in Computer and Information Science doi: 10.1007/978-3-642-14834-7_40 – volume: 35 start-page: 352 issue: 5–6 year: 2002 ident: 10.1016/j.cmpb.2020.105635_bib0098 article-title: Logistic regression and artificial neural network classification models: a methodology review publication-title: J. Biomed. Inform. doi: 10.1016/S1532-0464(03)00034-0 – year: 2010 ident: 10.1016/j.cmpb.2020.105635_bib0121 – start-page: 1 year: 2018 ident: 10.1016/j.cmpb.2020.105635_bib0008 article-title: A systematic map of data analytics in breast cancer – volume: 32 start-page: 2627 issue: 14–15 year: 1998 ident: 10.1016/j.cmpb.2020.105635_bib0119 article-title: Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences publication-title: Atmos. Environ. doi: 10.1016/S1352-2310(97)00447-0 – ident: 10.1016/j.cmpb.2020.105635_bib0049 – year: 2018 ident: 10.1016/j.cmpb.2020.105635_bib0095 article-title: Performance Analysis of Various Missing Value Imputation Methods on Heart Failure Dataset publication-title: Lecture Notes in Networks and Systems doi: 10.1007/978-3-319-56991-8_31 – year: 2019 ident: 10.1016/j.cmpb.2020.105635_bib0066 article-title: Evaluation of Classification Algorithms, Linear Discriminant Analysis and a New Hybrid Feature Selection Methodology for the Diagnosis of Coronary Artery Disease – volume: 80 start-page: 141 issue: 2 year: 2005 ident: 10.1016/j.cmpb.2020.105635_bib0110 article-title: Improved classification of medical data using abductive network committees trained on different feature subsets publication-title: Comput. Methods Programs Biomed. doi: 10.1016/j.cmpb.2005.08.001 – year: 2019 ident: 10.1016/j.cmpb.2020.105635_bib0117 article-title: Identification of significant features and data mining techniques in predicting heart disease publication-title: Telemat. Informatics doi: 10.1016/j.tele.2018.11.007 – volume: 11 start-page: 162 issue: 2 year: 2014 ident: 10.1016/j.cmpb.2020.105635_bib0022 article-title: Issues in the mining of heart failure datasets publication-title: Int. J. Autom. Comput. doi: 10.1007/s11633-014-0778-5 – start-page: 481 year: 2007 ident: 10.1016/j.cmpb.2020.105635_bib0097 article-title: Feature-weighted k-nearest neighbor classifier – volume: 86 start-page: 33 year: 2015 ident: 10.1016/j.cmpb.2020.105635_bib0074 article-title: Recent advances and emerging challenges of feature selection in the context of big data publication-title: Knowledge-Based Syst doi: 10.1016/j.knosys.2015.05.014 – year: 2008 ident: 10.1016/j.cmpb.2020.105635_bib0093 article-title: Cost-Sensitive Learning and the Class Imbalance Problem publication-title: Encycl. Mach. Learn. – volume: 112 start-page: 720 issue: 3 year: 2013 ident: 10.1016/j.cmpb.2020.105635_bib0061 article-title: Multistage approach for clustering and classification of ECG data publication-title: Comput. Methods Programs Biomed. doi: 10.1016/j.cmpb.2013.08.002 – volume: 45 start-page: 1051 issue: 4ve year: 2007 ident: 10.1016/j.cmpb.2020.105635_bib0045 article-title: Guidelines for performing Systematic Literature reviews in Software Engineering Version 2.3 publication-title: Engineering – volume: 97 start-page: 12 year: 2017 ident: 10.1016/j.cmpb.2020.105635_bib0010 article-title: Knowledge discovery in cardiology: A systematic literature review publication-title: Int. J. Med. Inform. doi: 10.1016/j.ijmedinf.2016.09.005 – ident: 10.1016/j.cmpb.2020.105635_bib0048 – start-page: 373 year: 2004 ident: 10.1016/j.cmpb.2020.105635_bib0108 article-title: Noise identification with the k-means algorithm publication-title: 16th IEEE Int. Conf. Tools with Artif. Intell. doi: 10.1109/ICTAI.2004.93 – start-page: 1142 year: 2018 ident: 10.1016/j.cmpb.2020.105635_bib0007 article-title: Data Mining Techniques in Diabetes Self-management: A Systematic Map – volume: 412 start-page: 5909 issue: 42 year: 2011 ident: 10.1016/j.cmpb.2020.105635_bib0065 article-title: Fuzzy quartile encoding as a preprocessing method for biomedical pattern classification publication-title: Theor. Comput. Sci. doi: 10.1016/j.tcs.2011.05.043 – volume: 53 start-page: 1531 issue: 8 year: 2006 ident: 10.1016/j.cmpb.2020.105635_bib0077 article-title: An association rule mining-based methodology for automated detection of ischemic ECG beats publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2006.873753 – volume: 58 start-page: 206 year: 2015 ident: 10.1016/j.cmpb.2020.105635_bib0052 article-title: Analogy-based software development effort estimation: A systematic mapping and review publication-title: Information and Software Technology doi: 10.1016/j.infsof.2014.07.013 – year: 2018 ident: 10.1016/j.cmpb.2020.105635_bib0043 article-title: Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis publication-title: Med. Image Anal. doi: 10.1016/j.media.2017.11.008 – year: 2006 ident: 10.1016/j.cmpb.2020.105635_bib0078 article-title: The impact of preprocessing on data mining: An evaluation of classifier sensitivity in direct marketing publication-title: Eur. J. Oper. Res. doi: 10.1016/j.ejor.2005.07.023 – year: 2019 ident: 10.1016/j.cmpb.2020.105635_bib0038 article-title: Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network publication-title: Nature Medicine – start-page: 3656 year: 2017 ident: 10.1016/j.cmpb.2020.105635_bib0005 article-title: Wrapper method for feature selection to classify cardiac arrhythmia – volume: 118 start-page: 151 year: 2016 ident: 10.1016/j.cmpb.2020.105635_bib0050 article-title: Systematic literature review of ensemble effort estimation publication-title: J. Syst. Softw. doi: 10.1016/j.jss.2016.05.016 – year: 2014 ident: 10.1016/j.cmpb.2020.105635_bib0123 – volume: 34 start-page: 513 issue: 2 year: 2014 ident: 10.1016/j.cmpb.2020.105635_bib0036 article-title: Effect of Multiscale PCA De-noising in ECG Beat Classification for Diagnosis of Cardiovascular Diseases publication-title: Circuits, Syst. Signal Process. doi: 10.1007/s00034-014-9864-8 – year: 2019 ident: 10.1016/j.cmpb.2020.105635_bib0086 article-title: Novel deep genetic ensemble of classifiers for arrhythmia detection using ECG signals publication-title: Neural Comput. Appl. – volume: 29 issue: 06 year: 2017 ident: 10.1016/j.cmpb.2020.105635_bib0020 article-title: APPLICATION OF ENSEMBLE ALGORITHM INTEGRATING MULTIPLE CRITERIA FEATURE SELECTION IN CORONARY HEART DISEASE DETECTION publication-title: Biomed. Eng. Appl. Basis Commun. doi: 10.4015/S1016237217500430 – volume: 72 year: 2015 ident: 10.1016/j.cmpb.2020.105635_bib0070 – volume: 10 start-page: 1082 issue: 6 year: 2016 ident: 10.1016/j.cmpb.2020.105635_bib0057 article-title: Impact of preprocessing on medical data classification publication-title: Front. Comput. Sci. doi: 10.1007/s11704-016-5203-5 – ident: 10.1016/j.cmpb.2020.105635_bib0088 – volume: 37 start-page: 3177 issue: 4 year: 2010 ident: 10.1016/j.cmpb.2020.105635_bib0111 article-title: A comparison of feature selection models utilizing binary particle swarm optimization and genetic algorithm in determining coronary artery disease using support vector machine publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2009.09.064 – year: 2018 ident: 10.1016/j.cmpb.2020.105635_bib0064 article-title: Time series and morphological feature extraction for classifying coronary artery disease from photoplethysmogram – volume: 8 start-page: 731 year: 2017 ident: 10.1016/j.cmpb.2020.105635_bib0100 article-title: Feature selection based on CHI square in artificial neural network to predict the accuracy of student study period publication-title: Int. J. Civ. Eng. Technol. – volume: 243 start-page: 599 year: 2014 ident: 10.1016/j.cmpb.2020.105635_bib0034 article-title: RF-SEA-Based Feature Selection for Data Classification in Medical Domain publication-title: Intell. Comput. Networking, Informatics doi: 10.1007/978-81-322-1665-0_59 – volume: 64 start-page: 1 year: 2015 ident: 10.1016/j.cmpb.2020.105635_bib0047 article-title: Guidelines for conducting systematic mapping studies in software engineering: An update publication-title: Information and Software Technology doi: 10.1016/j.infsof.2015.03.007 – start-page: 1 year: 2015 ident: 10.1016/j.cmpb.2020.105635_bib0031 article-title: Computational intelligence technique for early diagnosis of heart disease – volume: 12 start-page: 667 issue: 5 year: 2008 ident: 10.1016/j.cmpb.2020.105635_bib0067 article-title: Classification of electrocardiogram signals with support vector machines and particle swarm optimization publication-title: IEEE Trans. Inf. Technol. Biomed. doi: 10.1109/TITB.2008.923147 – year: 2018 ident: 10.1016/j.cmpb.2020.105635_bib0118 article-title: Application of machine learning in disease prediction – volume: 250 start-page: 113 year: 2013 ident: 10.1016/j.cmpb.2020.105635_bib0096 article-title: An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics publication-title: Inf. Sci. (Ny). doi: 10.1016/j.ins.2013.07.007 – volume: 116 start-page: 38 year: 2013 ident: 10.1016/j.cmpb.2020.105635_bib0035 article-title: ECG arrhythmia classification using a probabilistic neural network with a feature reduction method publication-title: Neurocomputing doi: 10.1016/j.neucom.2011.10.045 – volume: 50 start-page: 105 issue: 2 year: 2010 ident: 10.1016/j.cmpb.2020.105635_bib0060 article-title: Missing data imputation using statistical and machine learning methods in a real breast cancer problem publication-title: Artif. Intell. Med. doi: 10.1016/j.artmed.2010.05.002 – volume: 11 start-page: 47 issue: 2 year: 2013 ident: 10.1016/j.cmpb.2020.105635_bib0099 article-title: Artificial neural networks in medical diagnosis publication-title: Journal of Applied Biomedicine doi: 10.2478/v10136-012-0031-x – volume: 90 start-page: 125 year: 2017 ident: 10.1016/j.cmpb.2020.105635_bib0114 article-title: Optimal feature selection using a modified differential evolution algorithm and its effectiveness for prediction of heart disease publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2017.09.011 – ident: 10.1016/j.cmpb.2020.105635_bib0004 – volume: 73 start-page: 220 year: 2017 ident: 10.1016/j.cmpb.2020.105635_bib0017 article-title: Learning from class-imbalanced data: Review of methods and applications publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2016.12.035 – volume: 71 start-page: 31 year: 2017 ident: 10.1016/j.cmpb.2020.105635_bib0051 article-title: A systematic review of gamification in e-Health publication-title: J. Biomed. Inform. doi: 10.1016/j.jbi.2017.05.011 – year: 2010 ident: 10.1016/j.cmpb.2020.105635_bib0069 article-title: Enhanced Prediction of Heart Disease with Feature Subset Selection using Genetic Algorithm publication-title: Int. J. Eng. Sci. Technol. – start-page: 391 year: 2020 ident: 10.1016/j.cmpb.2020.105635_bib0024 article-title: Impact of Threshold Values for Filter-based Univariate Feature Selection in Heart Disease Classification – start-page: 1 year: 2006 ident: 10.1016/j.cmpb.2020.105635_bib0037 article-title: ECG Arrhythmias Recognition System Based on Independent Component Analysis Feature Extraction – volume: 46 start-page: 165 issue: 2 year: 2009 ident: 10.1016/j.cmpb.2020.105635_bib0084 article-title: Noise-tolerant electrocardiogram beat classification based on higher order statistics of subband components publication-title: Artif. Intell. Med. doi: 10.1016/j.artmed.2008.11.004 – year: 1999 ident: 10.1016/j.cmpb.2020.105635_bib0025 article-title: Feature Extraction Construction and Selection: A Data Mining Perspective publication-title: J. Am. Stat. Assoc. doi: 10.2307/2669967 – year: 2018 ident: 10.1016/j.cmpb.2020.105635_bib0116 article-title: Classification of imbalanced ECG beats using re-sampling techniques and AdaBoost ensemble classifier publication-title: Biomed. Signal Process. Control doi: 10.1016/j.bspc.2017.12.004 – year: 2018 ident: 10.1016/j.cmpb.2020.105635_bib0068 article-title: Novel methodology of cardiac health recognition based on ECG signals and evolutionary-neural system publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2017.09.022 – start-page: 2840 year: 2012 ident: 10.1016/j.cmpb.2020.105635_bib0062 article-title: A comparative study of missing value imputation with multiclass classification for clinical heart failure data – volume: 5 start-page: 1205 year: 2004 ident: 10.1016/j.cmpb.2020.105635_bib0021 article-title: Efficient Feature Selection via Analysis of Relevance and Redundancy publication-title: J. Mach. Learn. Res. – year: 2019 ident: 10.1016/j.cmpb.2020.105635_bib0091 article-title: A new machine learning technique for an accurate diagnosis of coronary artery disease publication-title: Comput. Methods Programs Biomed. doi: 10.1016/j.cmpb.2019.104992 – volume: 9 issue: 45 year: 2016 ident: 10.1016/j.cmpb.2020.105635_bib0013 article-title: A Multi-objective Non-Dominated Sorted Artificial Bee Colony Feature Selection Algorithm for Medical Datasets publication-title: Indian J. Sci. Technol. doi: 10.17485/ijst/2016/v9i45/102290 – volume: 46 start-page: 105 issue: 1 year: 1999 ident: 10.1016/j.cmpb.2020.105635_bib0094 article-title: From computing with numbers to computing with words - From manipulation of measurements to manipulation of perceptions publication-title: IEEE Trans. Circuits Syst. I Fundam. Theory Appl. doi: 10.1109/81.739259 – volume: 3 start-page: 571 issue: 4 year: 2005 ident: 10.1016/j.cmpb.2020.105635_bib0032 article-title: Support vector machine-based arrhythmia classification using reduced features publication-title: Int. J. Control. Autom. Syst. – volume: 1 start-page: 323 year: 2012 ident: 10.1016/j.cmpb.2020.105635_bib0102 article-title: A comparative study on the effect of feature selection on classification accuracy publication-title: Procedia Technol doi: 10.1016/j.protcy.2012.02.068 – volume: 44 start-page: 51 issue: 1 year: 2008 ident: 10.1016/j.cmpb.2020.105635_bib0072 article-title: Support vector machine-based arrhythmia classification using reduced features of heart rate variability signal publication-title: Artif. Intell. Med. doi: 10.1016/j.artmed.2008.04.007 – volume: 11 start-page: 25 issue: 1 year: 2015 ident: 10.1016/j.cmpb.2020.105635_bib0103 article-title: Effectiveness of Support Vector Machines in Medical Data mining publication-title: J. Commun. Softw. Syst. doi: 10.24138/jcomss.v11i1.114 – volume: 41 start-page: 4434 issue: 9 year: 2014 ident: 10.1016/j.cmpb.2020.105635_bib0009 article-title: Knowledge discovery in medicine: Current issue and future trend publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2014.01.011 – volume: 117 start-page: 595 year: 2016 ident: 10.1016/j.cmpb.2020.105635_bib0059 article-title: Missing data techniques in analogy-based software development effort estimation publication-title: J. Syst. Softw. doi: 10.1016/j.jss.2016.04.058 – volume: 37 start-page: 8659 issue: 12 year: 2010 ident: 10.1016/j.cmpb.2020.105635_bib0104 article-title: EEG signal classification using PCA, ICA, LDA and support vector machines publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2010.06.065 – year: 2017 ident: 10.1016/j.cmpb.2020.105635_bib0071 article-title: Dimension reduction methods for microarray data: a review publication-title: AIMS Bioeng doi: 10.3934/bioeng.2017.1.179 – volume: 37 start-page: 8102 issue: 12 year: 2010 ident: 10.1016/j.cmpb.2020.105635_bib0109 article-title: Hybrid prediction model for Type-2 diabetic patients publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2010.05.078 – year: 2017 ident: 10.1016/j.cmpb.2020.105635_bib0027 article-title: Deep learning for cardiac computer-aided diagnosis: benefits, issues & solutions publication-title: mHealth doi: 10.21037/mhealth.2017.09.01 – ident: 10.1016/j.cmpb.2020.105635_bib0101 – volume: 29 start-page: 660 issue: 7 year: 2015 ident: 10.1016/j.cmpb.2020.105635_bib0073 article-title: Multiclass Support Vector Machines for Classification of ECG Data with Missing Values publication-title: Appl. Artif. Intell. doi: 10.1080/08839514.2015.1051887 – volume: 2 start-page: 1 issue: 10 year: 2012 ident: 10.1016/j.cmpb.2020.105635_bib0056 article-title: Study and Development of Novel Feature Selection Framework for Heart Disease Prediction publication-title: Int. J. Sci. Res. Publ. – volume: 36 start-page: 10367 issue: 7 year: 2009 ident: 10.1016/j.cmpb.2020.105635_bib0113 article-title: A new feature selection method on classification of medical datasets: Kernel F-score feature selection publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2009.01.041 – year: 2018 ident: 10.1016/j.cmpb.2020.105635_bib0039 article-title: Passive detection of atrial fibrillation using a commercially available smartwatch publication-title: JAMA Cardiol doi: 10.1001/jamacardio.2018.0136 – volume: 30 start-page: 590 issue: 6 year: 2016 ident: 10.1016/j.cmpb.2020.105635_bib0033 article-title: On the Influence of Class Noise in Medical Data Classification: Treatment Using Noise Filtering Methods publication-title: Appl. Artif. Intell. doi: 10.1080/08839514.2016.1193719 – year: 2015 ident: 10.1016/j.cmpb.2020.105635_bib0012 article-title: Tutorial on practical tips of the most influential data preprocessing algorithms in data mining publication-title: Knowledge-Based Systems – start-page: 1200 year: 2015 ident: 10.1016/j.cmpb.2020.105635_bib0019 article-title: A review of feature selection methods with applications – year: 2019 ident: 10.1016/j.cmpb.2020.105635_bib0041 article-title: Chest radiographs in congestive heart failure: Visualizing neural network learning publication-title: Radiology doi: 10.1148/radiol.2018180887 – volume: 3 start-page: 1114 issue: 6 year: 2013 ident: 10.1016/j.cmpb.2020.105635_bib0126 article-title: Decision Tree Analysis on J48 Algorithm for Data Mining publication-title: Int. J. Adv. Res. Comput. Sci. Softw. Eng. – volume: 162 start-page: 69 year: 2018 ident: 10.1016/j.cmpb.2020.105635_bib0026 article-title: A systematic map of medical data preprocessing in knowledge discovery publication-title: Comput. Methods Programs Biomed. doi: 10.1016/j.cmpb.2018.05.007 – volume: 21 start-page: 1 issue: 01 year: 2006 ident: 10.1016/j.cmpb.2020.105635_bib0015 article-title: A survey of Knowledge Discovery and Data Mining process models publication-title: Knowl. Eng. Rev. doi: 10.1017/S0269888906000737 – ident: 10.1016/j.cmpb.2020.105635_bib0087 – volume: 1 year: 2017 ident: 10.1016/j.cmpb.2020.105635_bib0006 article-title: Systematic mapping study of data mining–based empirical studies in cardiology publication-title: Health Informatics J – year: 2018 ident: 10.1016/j.cmpb.2020.105635_bib0042 article-title: Deep Learning for Prediction of Obstructive Disease From Fast Myocardial Perfusion SPECT: A Multicenter Study publication-title: JACC Cardiovasc. Imaging doi: 10.1016/j.jcmg.2018.01.020 – start-page: 1984 year: 2012 ident: 10.1016/j.cmpb.2020.105635_bib0107 article-title: Fault detection and diagnosis for spacecraft using principal component analysis and support vector machines – start-page: 44 year: 2007 ident: 10.1016/j.cmpb.2020.105635_bib0076 article-title: Medical Datamining with a New Algorithm for Feature Selection and Naive Bayesian Classifier – year: 2012 ident: 10.1016/j.cmpb.2020.105635_bib0090 article-title: Evaluating the Impact of Categorical Data Encoding and Scaling on Neural Network Classification Performance: The Case of Repeat Consumption of Identical Cultural Goods publication-title: Communications in Computer and Information Science doi: 10.1007/978-3-642-32909-8_35 – volume: 2 year: 2006 ident: 10.1016/j.cmpb.2020.105635_bib0105 article-title: Adaptive Intrusion Detection Based on Machine Learning: Feature Extraction, Classifier Construction and Sequential Pattern Prediction publication-title: Int. J. Web Serv. Pract. – volume: 207 year: 2006 ident: 10.1016/j.cmpb.2020.105635_bib0018 – year: 2018 ident: 10.1016/j.cmpb.2020.105635_bib0040 article-title: Fast and accurate view classification of echocardiograms using deep learning publication-title: npj Digit. Med. doi: 10.1038/s41746-017-0013-1 – volume: 66 start-page: 161 year: 2017 ident: 10.1016/j.cmpb.2020.105635_bib0063 article-title: MACE prediction of acute coronary syndrome via boosted resampling classification using electronic medical records publication-title: J. Biomed. Inform. doi: 10.1016/j.jbi.2017.01.001 – volume: 38 issue: 5 year: 2014 ident: 10.1016/j.cmpb.2020.105635_bib0002 article-title: A New Data Preparation Method Based on Clustering Algorithms for Diagnosis Systems of Heart and Diabetes Diseases publication-title: J. Med. Syst. doi: 10.1007/s10916-014-0048-7 – volume: 15 start-page: 33 issue: 1 year: 2011 ident: 10.1016/j.cmpb.2020.105635_bib0079 article-title: Diagnosis of cardiovascular abnormalities from compressed ECG: A data mining-based approach publication-title: IEEE Trans. Inf. Technol. Biomed. doi: 10.1109/TITB.2010.2094197 – start-page: 193 year: 2009 ident: 10.1016/j.cmpb.2020.105635_bib0092 article-title: Learning pattern classification tasks with imbalanced data sets publication-title: Pattern Recognit – start-page: 592 year: 2005 ident: 10.1016/j.cmpb.2020.105635_bib0106 article-title: Effect Analysis of Dimension Reduction on Support Vector Machines – volume: 43 start-page: 17 issue: 1 year: 2019 ident: 10.1016/j.cmpb.2020.105635_bib0011 article-title: A Systematic Mapping Study of Data Preparation in Heart Disease Knowledge Discovery publication-title: J. Med. Syst. doi: 10.1007/s10916-018-1134-z – year: 2017 ident: 10.1016/j.cmpb.2020.105635_bib0028 article-title: Deep learning for healthcare: Review, opportunities and challenges publication-title: Brief. Bioinform. – volume: 21 start-page: 331 issue: 4 year: 2000 ident: 10.1016/j.cmpb.2020.105635_bib0120 article-title: Back-propagation learning in improving the accuracy of neural network-based tourism demand forecasting publication-title: Tour. Manag. doi: 10.1016/S0261-5177(99)00067-9 – volume: 45 start-page: 999 issue: 5 year: 2012 ident: 10.1016/j.cmpb.2020.105635_bib0112 article-title: Decision-making model for early diagnosis of congestive heart failure using rough set and decision tree approaches publication-title: J. Biomed. Inform. doi: 10.1016/j.jbi.2012.04.013 – volume: 43 start-page: 2222 issue: 12 year: 2013 ident: 10.1016/j.cmpb.2020.105635_bib0054 article-title: A threshold fuzzy entropy based feature selection for medical database classification publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2013.10.016 |
| SSID | ssj0002556 |
| Score | 2.573666 |
| SecondaryResourceType | review_article |
| Snippet | •A systematic review on the use of data preprocessing techniques for heart disease classification purpose was conducted.•A total of 49 studies published... Early detection of heart disease is an important challenge since 17.3 million people yearly lose their lives due to heart diseases. Besides, any error in... |
| SourceID | proquest pubmed crossref elsevier |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 105635 |
| SubjectTerms | Cardiac datasets Cardiology Data Mining Data preprocessing Datamining Heart Diseases - diagnosis Humans Literature review |
| Title | Data preprocessing for heart disease classification: A systematic literature review |
| URI | https://www.clinicalkey.com/#!/content/1-s2.0-S0169260720314681 https://dx.doi.org/10.1016/j.cmpb.2020.105635 https://www.ncbi.nlm.nih.gov/pubmed/32652383 https://www.proquest.com/docview/2423056619 |
| Volume | 195 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1LS8NAEF6KgngR39ZHWcGbxCbZPL2VaqmKvdRCb8tuNoFKTYOmV3-7M9lNiqAVPCbsJGF2MvMt880MIVcQRVKIu76lbBFYXprElnAxyRvbKpZeCIi7YluMguHEe5z60xbp17UwSKs0vl_79Mpbmztdo81uMZt1x9hHBNA45hGxfqiqYPdCtPKbzxXNA1ts6f7esYWrTeGM5nglb4WEM6KrB9BXI99-DE6_gc8qCA12yY5Bj7SnP3CPtNJ8n2w9m_z4ARnfiVLQAjtVVvx_iEsUUCnFsdUlNckYmiBiRopQtSu3tEdX_ZzpvOmzTHVZyyGZDO5f-kPLjE2wEi9kpZVlcCgSgXITAEeMhVJlAWJoFYgwdGSUITXNhcgUw-lQBTFzRAJexvFDFcVOlLEjspEv8vSEUBXJTLLUT4WtPD-Kpe1KgEzwQMdXiR20iVPriyempziOtpjzmjz2ylHHHHXMtY7b5LqRKXRHjbWrWb0NvK4VBe_GweGvlfIbqW_W9KfcZb3THH4zzJ2IPF0sPzjCTlgCmm2TY20CzdcDAobjfMRO__nWM7KNV5oieE42yvdlegFQp5SdypY7ZLP38DQcfQHMAPoV |
| linkProvider | Elsevier |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1LS8NAEB6kgnoR39bnCt4kNO-Ht1Itrba91EJvy242gYrGovH_O5PdRAQf4DXZScLsZuYbZuYbgEv0Ihn63cBStggtP0sTS7iU5E1slUg_QsRdVVtMwsHMv5sH8xXo1b0wVFZpbL-26ZW1Nlc6Rpud5WLRmRKPCKJxyiNS_xCGQKvEThW0YLU7vB9MGoNMLFua4juxSMD0zugyr_R5KTFMdPUM-mrq27f-6Sf8Wfmh_hZsGgDJuvobt2ElK3ZgbWxS5LswvRGlYEsiq6xaANA1MQSmjCZXl8zkY1hKoJmqhKqNuWZd9knpzJ4aqmWmO1v2YNa_fegNLDM5wUr9yCutPMe4SITKTREfeV4kVR4SjFahiCJHxjlVp7nonBIMEFWYeI5I0dA4QaTixIlzbx9axUuRHQJTscyllwWZsJUfxIm0XYmoCR_oBCq1wzY4tb54amjFabrFE6_rxx456ZiTjrnWcRuuGpmlJtX4dbVXbwOv20XRwHG0-b9KBY3UlwP1p9xFvdMc_zRKn4gie3l_44Q8cQlqtg0H-gg0X48gGCP62Dv651vPYX3wMB7x0XByfwwbdEdXDJ5Aq3x9z04R-ZTyzJzsD2JY_MY |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Data+preprocessing+for+heart+disease+classification%3A+A+systematic+literature+review&rft.jtitle=Computer+methods+and+programs+in+biomedicine&rft.au=Benhar%2C+H&rft.au=Idri%2C+A&rft.au=Fern%C3%A1ndez-Alem%C3%A1n%2C+J+L&rft.date=2020-10-01&rft.eissn=1872-7565&rft.volume=195&rft.spage=105635&rft_id=info:doi/10.1016%2Fj.cmpb.2020.105635&rft_id=info%3Apmid%2F32652383&rft.externalDocID=32652383 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0169-2607&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0169-2607&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0169-2607&client=summon |