Diagnosis of Acute Coronary Syndrome with a Support Vector Machine
Acute coronary syndrome (ACS) is a serious condition arising from an imbalance of supply and demand to meet myocardium’s metabolic needs. Patients typically present with retrosternal chest pain radiating to neck and left arm. Electrocardiography (ECG) and laboratory tests are used indiagnosis. Howev...
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Published in | Journal of medical systems Vol. 40; no. 4; p. 84 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
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New York
Springer US
01.04.2016
Springer Nature B.V |
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Abstract | Acute coronary syndrome (ACS) is a serious condition arising from an imbalance of supply and demand to meet myocardium’s metabolic needs. Patients typically present with retrosternal chest pain radiating to neck and left arm. Electrocardiography (ECG) and laboratory tests are used indiagnosis. However in emergency departments, there are some difficulties for physicians to decide whether hospitalizing, following up or discharging the patient. The aim of the study is to diagnose ACS and helping the physician with his decisionto discharge or to hospitalizevia machine learning techniques such as support vector machine (SVM) by using patient data including age, sex, risk factors, and cardiac enzymes (CK-MB, Troponin I) of patients presenting to emergency department with chest pain. Clinical, laboratory, and imaging data of 228 patients presenting to emergency department with chest pain were reviewedand the performance of support vector machine. Four different methods (Support vector machine (SVM), Artificial neural network (ANN), Naïve Bayes and Logistic Regression) were tested and the results of SVM which has the highest accuracy is reported. Among 228 patients aged 19 to 91 years who were included in the study, 99 (43.4 %) were qualified as ACS, while 129 (56.5 %) had no ACS. The classification model using SVM attained a 99.13 % classification success. The present study showed a 99.13 % classification success for ACS diagnosis attained by Support Vector Machine. This study showed that machine learning techniques may help emergency department staff make decisions by rapidly producing relevant data. |
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AbstractList | Acute coronary syndrome (ACS) is a serious condition arising from an imbalance of supply and demand to meet myocardium's metabolic needs. Patients typically present with retrosternal chest pain radiating to neck and left arm. Electrocardiography (ECG) and laboratory tests are used indiagnosis. However in emergency departments, there are some difficulties for physicians to decide whether hospitalizing, following up or discharging the patient. The aim of the study is to diagnose ACS and helping the physician with his decisionto discharge or to hospitalizevia machine learning techniques such as support vector machine (SVM) by using patient data including age, sex, risk factors, and cardiac enzymes (CK-MB, Troponin I) of patients presenting to emergency department with chest pain. Clinical, laboratory, and imaging data of 228 patients presenting to emergency department with chest pain were reviewedand the performance of support vector machine. Four different methods (Support vector machine (SVM), Artificial neural network (ANN), Naïve Bayes and Logistic Regression) were tested and the results of SVM which has the highest accuracy is reported. Among 228 patients aged 19 to 91 years who were included in the study, 99 (43.4 %) were qualified as ACS, while 129 (56.5 %) had no ACS. The classification model using SVM attained a 99.13 % classification success. The present study showed a 99.13 % classification success for ACS diagnosis attained by Support Vector Machine. This study showed that machine learning techniques may help emergency department staff make decisions by rapidly producing relevant data. Acute coronary syndrome (ACS) is a serious condition arising from an imbalance of supply and demand to meet myocardium's metabolic needs. Patients typically present with retrosternal chest pain radiating to neck and left arm. Electrocardiography (ECG) and laboratory tests are used in diagnosis. However in emergency departments, there are some difficulties for physicians to decide whether hospitalizing, following up or discharging the patient. The aim of the study is to diagnose ACS and helping the physician with his decision to discharge or to hospitalize via machine learning techniques such as support vector machine (SVM) by using patient data including age, sex, risk factors, and cardiac enzymes (CK-MB, Troponin I) of patients presenting to emergency department with chest pain. Clinical, laboratory, and imaging data of 228 patients presenting to emergency department with chest pain were reviewed and the performance of support vector machine. Four different methods (Support vector machine (SVM), Artificial neural network (ANN), Naïve Bayes and Logistic Regression) were tested and the results of SVM which has the highest accuracy is reported. Among 228 patients aged 19 to 91 years who were included in the study, 99 (43.4 %) were qualified as ACS, while 129 (56.5 %) had no ACS. The classification model using SVM attained a 99.13 % classification success. The present study showed a 99.13 % classification success for ACS diagnosis attained by Support Vector Machine. This study showed that machine learning techniques may help emergency department staff make decisions by rapidly producing relevant data. Acute coronary syndrome (ACS) is a serious condition arising from an imbalance of supply and demand to meet myocardium’s metabolic needs. Patients typically present with retrosternal chest pain radiating to neck and left arm. Electrocardiography (ECG) and laboratory tests are used indiagnosis. However in emergency departments, there are some difficulties for physicians to decide whether hospitalizing, following up or discharging the patient. The aim of the study is to diagnose ACS and helping the physician with his decisionto discharge or to hospitalizevia machine learning techniques such as support vector machine (SVM) by using patient data including age, sex, risk factors, and cardiac enzymes (CK-MB, Troponin I) of patients presenting to emergency department with chest pain. Clinical, laboratory, and imaging data of 228 patients presenting to emergency department with chest pain were reviewedand the performance of support vector machine. Four different methods (Support vector machine (SVM), Artificial neural network (ANN), Naïve Bayes and Logistic Regression) were tested and the results of SVM which has the highest accuracy is reported. Among 228 patients aged 19 to 91 years who were included in the study, 99 (43.4 %) were qualified as ACS, while 129 (56.5 %) had no ACS. The classification model using SVM attained a 99.13 % classification success. The present study showed a 99.13 % classification success for ACS diagnosis attained by Support Vector Machine. This study showed that machine learning techniques may help emergency department staff make decisions by rapidly producing relevant data. |
ArticleNumber | 84 |
Author | Yildiz, Oktay Berikol, Göksu Bozdereli Özcan, İ. Türkay |
Author_xml | – sequence: 1 givenname: Göksu Bozdereli surname: Berikol fullname: Berikol, Göksu Bozdereli email: gokxsu@hotmail.com organization: Karaman Public Hospital, Department of Emergency Medicine KARAMAN, Karaman Public Hospital, Computer Engineering Dept, Gazi University Faculty of Engineering, Faculty of Medicine, Dept. of Cardiology MERSİN, Mersin University Research and Training Hospital – sequence: 2 givenname: Oktay surname: Yildiz fullname: Yildiz, Oktay organization: Karaman Public Hospital, Department of Emergency Medicine KARAMAN, Karaman Public Hospital, Computer Engineering Dept, Gazi University Faculty of Engineering, Faculty of Medicine, Dept. of Cardiology MERSİN, Mersin University Research and Training Hospital – sequence: 3 givenname: İ. Türkay surname: Özcan fullname: Özcan, İ. Türkay organization: Karaman Public Hospital, Department of Emergency Medicine KARAMAN, Karaman Public Hospital, Computer Engineering Dept, Gazi University Faculty of Engineering, Faculty of Medicine, Dept. of Cardiology MERSİN, Mersin University Research and Training Hospital |
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Cites_doi | 10.1002/hfm.20134 10.1016/j.eswa.2010.10.050 10.1161/CIR.0b013e3182009701 10.1016/j.jacc.2014.09.017 10.9756/BIJSESC.4336 10.1080/10556780512331318164 10.1007/s10916-010-9535-7 10.2147/CEOR.S43672 10.1056/NEJM198803313181301 10.1016/j.jacc.2009.09.071 10.1093/eurheartj/eht356 10.1161/CIR.0000000000000152 10.1007/s10916-011-9762-6 10.1016/0933-3657(95)00018-6 10.1016/j.artmed.2006.07.006 10.1007/978-3-642-27443-5_25 |
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Keywords | Support vector machine Acute coronary syndrome Artificial intelligence Machine learning |
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References | Chitra, Seenivasagam (CR23) 2013; 3 Aj, Schölkopf (CR15) 2004; 14 Conforti, Guido (CR18) 2005; 20 Mozaffarian, Benjamin, Go, Arnett, Blaha, Cushman, Huffman (CR4) 2015; 131 CR5 CR19 Nichols, Townsend, Scarborough, Rayner (CR1) 2013; 34 Akay, Toksarı (CR16) 2009; 19 Green, Björk, Forberg, Ekelund (CR17) 2006; 38 Vadicherla, Sonawane (CR22) 2013; 2 Xie, Wangc (CR13) 2011; 38 CR21 Ha, Joo (CR20) 2010; 4 Scırıca (CR10) 2010; 55 Chen, Crivera, Stokes, Boulanger, Schein (CR2) 2013; 5 Kurz, Mattu, Brady (CR3) 2014 Ohmann, Moustakis, Yang, Lang (CR14) 1996; 8 Goldman, Cook, Brand, Lee, Rouan, Weisberg, Gottlieb (CR8) 1988; 318 Martis, Krishnan, Chakraborty, Pal, Sarkar (CR12) 2012; 36 Hsieh, Hsieh, Cheng, Chen, Hsu, Lee (CR11) 2012; 36 Roger (CR6) 2011; 123 Cruz, Wishart (CR9) 2006; 2 Amsterdam, Wenger, Brindis, Casey, Ganiats, Holmes, Levine (CR7) 2014; 64 8963379 - Artif Intell Med. 1996 Feb;8(1):23-36 16962295 - Artif Intell Med. 2006 Nov;38(3):305-18 21160056 - Circulation. 2011 Feb 1;123(4):e18-e209 23662068 - Clinicoecon Outcomes Res. 2013 May 01;5:181-8 24014390 - Eur Heart J. 2013 Oct;34(39):3028-34 20359589 - J Am Coll Cardiol. 2010 Apr 6;55(14):1403-15 3280998 - N Engl J Med. 1988 Mar 31;318(13):797-803 20703662 - J Med Syst. 2012 Apr;36(2):677-88 25520374 - Circulation. 2015 Jan 27;131(4):e29-322 25260718 - J Am Coll Cardiol. 2014 Dec 23;64(24):e139-228 26320110 - Eur Heart J. 2016 Jan 14;37(3):267-315 19458758 - Cancer Inform. 2007 Feb 11;2:59-77 21811801 - J Med Syst. 2012 Oct;36(5):2841-7 RJ Martis (432_CR12) 2012; 36 D Conforti (432_CR18) 2005; 20 SL Hsieh (432_CR11) 2012; 36 432_CR19 D Akay (432_CR16) 2009; 19 D Mozaffarian (432_CR4) 2015; 131 SH Ha (432_CR20) 2010; 4 432_CR5 M Nichols (432_CR1) 2013; 34 V Roger (432_CR6) 2011; 123 JA Cruz (432_CR9) 2006; 2 S Aj (432_CR15) 2004; 14 SY Chen (432_CR2) 2013; 5 L Goldman (432_CR8) 1988; 318 EA Amsterdam (432_CR7) 2014; 64 M Green (432_CR17) 2006; 38 D Vadicherla (432_CR22) 2013; 2 J Xie (432_CR13) 2011; 38 MC Kurz (432_CR3) 2014 R Chitra (432_CR23) 2013; 3 BM Scırıca (432_CR10) 2010; 55 C Ohmann (432_CR14) 1996; 8 432_CR21 |
References_xml | – volume: 19 start-page: 1 issue: 1 year: 2009 end-page: 14 ident: CR16 article-title: Ant Colony Optimization Approach For Classification Of Occupational Low Back Disorder Risks publication-title: Human Factors And Ergonomics İn Manufacturing doi: 10.1002/hfm.20134 contributor: fullname: Toksarı – ident: CR21 – volume: 38 start-page: 5809 year: 2011 end-page: 5815 ident: CR13 article-title: Using Support Vector Machines With A Novel Hybrid Feature Selection Method For Diagnosis Of Erythemato-Squamous Diseases publication-title: Expert Systems With Applications doi: 10.1016/j.eswa.2010.10.050 contributor: fullname: Wangc – ident: CR19 – volume: 2 start-page: 693 issue: 9 year: 2013 end-page: 701 ident: CR22 article-title: Classification Of Heart Disease Using Svm And Ann publication-title: Ijrcct contributor: fullname: Sonawane – volume: 123 start-page: 18 year: 2011 end-page: 209 ident: CR6 article-title: Aha Statistical Update. Heart Disease And Stroke Statistics—2011 Update. A Report From The American Heart Association publication-title: Circulation doi: 10.1161/CIR.0b013e3182009701 contributor: fullname: Roger – volume: 64 start-page: E139 issue: 24 year: 2014 end-page: E228 ident: CR7 article-title: 2014 Aha/Acc Guideline For The Management Of Patients With Non–St-Elevation Acute Coronary Syndromes: A Report Of The American College Of Cardiology/American Heart Association Task Force On Practice Guidelines publication-title: Journal Of The American College Of Cardiology doi: 10.1016/j.jacc.2014.09.017 contributor: fullname: Levine – volume: 3 start-page: 01 issue: 1 year: 2013 end-page: 07 ident: CR23 article-title: Heart Disease Prediction System Using Supervised Learning Classifier publication-title: Bonfring International Journal Of Software Engineering And Soft Computing doi: 10.9756/BIJSESC.4336 contributor: fullname: Seenivasagam – volume: 20 start-page: 401 issue: 2-3 year: 2005 end-page: 413 ident: CR18 article-title: Kernel-Based Support Vector Machine Classifiers For Early Detection Of Myocardial İnfarction publication-title: Optimization Methods And Software doi: 10.1080/10556780512331318164 contributor: fullname: Guido – volume: 2 start-page: 59 year: 2006 ident: CR9 article-title: Applications Of Machine Learning İn Cancer Prediction And Prognosis publication-title: Cancer Informat. contributor: fullname: Wishart – volume: 36 start-page: 677 year: 2012 end-page: 688 ident: CR12 article-title: Automated Screening Of Arrhythmia Using Wavelet Based Machine Learning Techniques publication-title: J. Med. Syst. doi: 10.1007/s10916-010-9535-7 contributor: fullname: Sarkar – volume: 5 start-page: 181 year: 2013 ident: CR2 article-title: Clinical And Economic Outcomes Among Hospitalized Patients With Acute Coronary Syndrome: An Analysis Of A National Representative Medicare Population publication-title: Clinicoeconomics And Outcomes Research: Ceor doi: 10.2147/CEOR.S43672 contributor: fullname: Schein – year: 2014 ident: CR3 publication-title: Acute Coronary Syndrome contributor: fullname: Brady – volume: 318 start-page: 797 issue: 13 year: 1988 end-page: 803 ident: CR8 article-title: A Computer Protocol To Predict Myocardial İnfarction İn Emergency Department Patients With Chest Pain publication-title: New England Journal Of Medicine doi: 10.1056/NEJM198803313181301 contributor: fullname: Gottlieb – volume: 55 start-page: 1403 issue: 14 year: 2010 end-page: 1415 ident: CR10 article-title: Acute Coronary Syndromeemerging Tools For Diagnosis And Risk Assessment publication-title: Journal Of The American College Of Cardiology doi: 10.1016/j.jacc.2009.09.071 contributor: fullname: Scırıca – volume: 14 start-page: 199 issue: 3 year: 2004 end-page: 222 ident: CR15 article-title: A Tutorial On Support Vector Regression publication-title: Neurocolt Technical Report contributor: fullname: Schölkopf – volume: 34 start-page: 3028 issue: 39 year: 2013 end-page: 3034 ident: CR1 article-title: Cardiovascular Disease İn Europe: Epidemiological Update publication-title: Eur. Heart J. doi: 10.1093/eurheartj/eht356 contributor: fullname: Rayner – ident: CR5 – volume: 4 start-page: 33 issue: 1 year: 2010 end-page: 38 ident: CR20 article-title: A Hybrid Data Mining Method For The Medical Classification Of Chest Pain publication-title: International Journal Of Computer And Information Engineering contributor: fullname: Joo – volume: 131 start-page: E29 issue: 4 year: 2015 ident: CR4 article-title: Heart Disease And Stroke Statistics-2015 Update: A Report From The American Heart Association publication-title: Circulation doi: 10.1161/CIR.0000000000000152 contributor: fullname: Huffman – volume: 36 start-page: 2841 year: 2012 end-page: 2847 ident: CR11 article-title: Design Ensemble Machine Learning Model For Breast Cancer Diagnosis publication-title: J. Med. Syst. doi: 10.1007/s10916-011-9762-6 contributor: fullname: Lee – volume: 8 start-page: 23 year: 1996 end-page: 36 ident: CR14 article-title: Evaluation Of Automatic Knowledge Acquisition Techniques İn The Diagnosis Of Acute Abdominal Pain publication-title: Artificial Intelligence İn Medicine doi: 10.1016/0933-3657(95)00018-6 contributor: fullname: Lang – volume: 38 start-page: 305 issue: 3 year: 2006 end-page: 318 ident: CR17 article-title: Comparison Between Neural Networks And Multiple Logistic Regression To Predict Acute Coronary Syndrome İn The Emergency Room publication-title: Artificial İntelligence İn Medicine doi: 10.1016/j.artmed.2006.07.006 contributor: fullname: Ekelund – volume: 14 start-page: 199 issue: 3 year: 2004 ident: 432_CR15 publication-title: Neurocolt Technical Report contributor: fullname: S Aj – ident: 432_CR19 – volume: 2 start-page: 693 issue: 9 year: 2013 ident: 432_CR22 publication-title: Ijrcct contributor: fullname: D Vadicherla – volume-title: Acute Coronary Syndrome year: 2014 ident: 432_CR3 contributor: fullname: MC Kurz – volume: 38 start-page: 305 issue: 3 year: 2006 ident: 432_CR17 publication-title: Artificial İntelligence İn Medicine doi: 10.1016/j.artmed.2006.07.006 contributor: fullname: M Green – volume: 34 start-page: 3028 issue: 39 year: 2013 ident: 432_CR1 publication-title: Eur. Heart J. doi: 10.1093/eurheartj/eht356 contributor: fullname: M Nichols – volume: 36 start-page: 2841 year: 2012 ident: 432_CR11 publication-title: J. Med. Syst. doi: 10.1007/s10916-011-9762-6 contributor: fullname: SL Hsieh – volume: 8 start-page: 23 year: 1996 ident: 432_CR14 publication-title: Artificial Intelligence İn Medicine doi: 10.1016/0933-3657(95)00018-6 contributor: fullname: C Ohmann – volume: 3 start-page: 01 issue: 1 year: 2013 ident: 432_CR23 publication-title: Bonfring International Journal Of Software Engineering And Soft Computing doi: 10.9756/BIJSESC.4336 contributor: fullname: R Chitra – volume: 20 start-page: 401 issue: 2-3 year: 2005 ident: 432_CR18 publication-title: Optimization Methods And Software doi: 10.1080/10556780512331318164 contributor: fullname: D Conforti – ident: 432_CR21 doi: 10.1007/978-3-642-27443-5_25 – volume: 123 start-page: 18 year: 2011 ident: 432_CR6 publication-title: Circulation doi: 10.1161/CIR.0b013e3182009701 contributor: fullname: V Roger – volume: 36 start-page: 677 year: 2012 ident: 432_CR12 publication-title: J. Med. Syst. doi: 10.1007/s10916-010-9535-7 contributor: fullname: RJ Martis – ident: 432_CR5 – volume: 2 start-page: 59 year: 2006 ident: 432_CR9 publication-title: Cancer Informat. contributor: fullname: JA Cruz – volume: 4 start-page: 33 issue: 1 year: 2010 ident: 432_CR20 publication-title: International Journal Of Computer And Information Engineering contributor: fullname: SH Ha – volume: 55 start-page: 1403 issue: 14 year: 2010 ident: 432_CR10 publication-title: Journal Of The American College Of Cardiology doi: 10.1016/j.jacc.2009.09.071 contributor: fullname: BM Scırıca – volume: 19 start-page: 1 issue: 1 year: 2009 ident: 432_CR16 publication-title: Human Factors And Ergonomics İn Manufacturing doi: 10.1002/hfm.20134 contributor: fullname: D Akay – volume: 64 start-page: E139 issue: 24 year: 2014 ident: 432_CR7 publication-title: Journal Of The American College Of Cardiology doi: 10.1016/j.jacc.2014.09.017 contributor: fullname: EA Amsterdam – volume: 131 start-page: E29 issue: 4 year: 2015 ident: 432_CR4 publication-title: Circulation doi: 10.1161/CIR.0000000000000152 contributor: fullname: D Mozaffarian – volume: 38 start-page: 5809 year: 2011 ident: 432_CR13 publication-title: Expert Systems With Applications doi: 10.1016/j.eswa.2010.10.050 contributor: fullname: J Xie – volume: 5 start-page: 181 year: 2013 ident: 432_CR2 publication-title: Clinicoeconomics And Outcomes Research: Ceor doi: 10.2147/CEOR.S43672 contributor: fullname: SY Chen – volume: 318 start-page: 797 issue: 13 year: 1988 ident: 432_CR8 publication-title: New England Journal Of Medicine doi: 10.1056/NEJM198803313181301 contributor: fullname: L Goldman |
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SubjectTerms | Acute Coronary Syndrome - diagnosis Acute coronary syndromes Adult Age Factors Aged Aged, 80 and over Artificial intelligence Bayes Theorem Chest Pain Creatine Kinase, MB Form - blood Decision Support Systems, Clinical Electrocardiography Emergency medical care Female Health Informatics Health Sciences Humans Logistic Models Male Medical diagnosis Medicine Medicine & Public Health Middle Aged Neural Networks (Computer) Reproducibility of Results Risk Factors Sex Factors Statistics for Life Sciences Support Vector Machine Transactional Processing Systems Troponin I - blood |
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