Improving the Accuracy for Analyzing Heart Diseases Prediction Based on the Ensemble Method
Heart disease is the deadliest disease and one of leading causes of death worldwide. Machine learning is playing an essential role in the medical side. In this paper, ensemble learning methods are used to enhance the performance of predicting heart disease. Two features of extraction methods: linear...
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| Published in | Complexity (New York, N.Y.) Vol. 2021; no. 1 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Hoboken
Hindawi
2021
John Wiley & Sons, Inc Wiley |
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| Abstract | Heart disease is the deadliest disease and one of leading causes of death worldwide. Machine learning is playing an essential role in the medical side. In this paper, ensemble learning methods are used to enhance the performance of predicting heart disease. Two features of extraction methods: linear discriminant analysis (LDA) and principal component analysis (PCA), are used to select essential features from the dataset. The comparison between machine learning algorithms and ensemble learning methods is applied to selected features. The different methods are used to evaluate models: accuracy, recall, precision, F-measure, and ROC.The results show the bagging ensemble learning method with decision tree has achieved the best performance. |
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| AbstractList | Heart disease is the deadliest disease and one of leading causes of death worldwide. Machine learning is playing an essential role in the medical side. In this paper, ensemble learning methods are used to enhance the performance of predicting heart disease. Two features of extraction methods: linear discriminant analysis (LDA) and principal component analysis (PCA), are used to select essential features from the dataset. The comparison between machine learning algorithms and ensemble learning methods is applied to selected features. The different methods are used to evaluate models: accuracy, recall, precision, F-measure, and ROC.The results show the bagging ensemble learning method with decision tree has achieved the best performance. |
| Author | Amin Ali, Abdelmegeid Anwar, Eman M. Gao, Xiao-Yan Shaban Hassan, Hassan |
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| Cites_doi | 10.1109/ICTCS.2019.8923053 10.1109/BigData47090.2019.9005488 10.3233/JIFS-179566 10.1155/2018/3860146 10.1007/978-981-15-5341-7_78 10.1007/978-981-13-0059-2_8 10.3390/e21080763 10.1109/ICEENG45378.2020.9171739 10.1016/j.eswa.2012.08.028 10.31838/ijpr/2020.12.04.013 10.1109/CSIBIG.2014.7056973 10.5539/cis.v6n3p48 10.5120/2368-3115 10.1007/978-981-15-6318-8_19 10.1111/jcpe.13189 10.1007/s42978-019-0001-z 10.1109/ICIMIA48430.2020.9074954 10.4236/jsea.2014.712093 10.1016/j.knosys.2016.09.032 10.1016/j.imu.2020.100330 10.1016/j.acvd.2019.10.008 10.1007/978-981-15-1097-7_76 10.1177/1460458219899210 10.1109/ISCC.2017.8024530 10.14419/ijet.v7i4.28646 |
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| Copyright | Copyright © 2021 Xiao-Yan Gao et al. Copyright © 2021 Xiao-Yan Gao et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 |
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| Snippet | Heart disease is the deadliest disease and one of leading causes of death worldwide. Machine learning is playing an essential role in the medical side. In this... |
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| SubjectTerms | Accuracy Age Algorithms Angina pectoris Back propagation Blood pressure Cardiovascular disease Data collection Data mining Datasets Decision trees Discriminant analysis Feature extraction Feature selection Heart diseases Heart rate Literature reviews Machine learning Model accuracy Neural networks Pain Performance prediction Principal components analysis Support vector machines |
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| Title | Improving the Accuracy for Analyzing Heart Diseases Prediction Based on the Ensemble Method |
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