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Open AccessArticle

Coronary Artery Disease Diagnosis; Ranking the Significant Features Using a Random Trees Model

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Department of Computer Engineering, Faculty of Engineering, University of Birjand, Birjand 97175/615, Iran
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Department of Nursing, School of Nursing and Allied Medical Sciences, Maragheh Faculty of Medical Sciences, Maragheh, Iran
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Department of Electronics, Faculty of Electrical and Computer Engineering, University of Birjand, Birjand 9717434765, Iran
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Kalman Kando Faculty of Electrical Engineering, Obuda University, 1034 Budapest, Hungary
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Institute of Structural Mechanics, Bauhaus Universität-Weimar, D-99423 Weimar, Germany
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Department of Mathematics and Informatics, J. Selye University, 94501 Komarno, Slovakia
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Faculty of Health, Queensland University of Technology, 130 Victoria Park Road, Queensland 4059, Australia
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Department Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
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Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam
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Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam
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Authors to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2020, 17(3), 731; https://doi.org/10.3390/ijerph17030731
Received: 20 December 2019 / Revised: 15 January 2020 / Accepted: 20 January 2020 / Published: 23 January 2020
(This article belongs to the Special Issue Disease Prediction, Machine Learning, and Healthcare)
Heart disease is one of the most common diseases in middle-aged citizens. Among the vast number of heart diseases, coronary artery disease (CAD) is considered as a common cardiovascular disease with a high death rate. The most popular tool for diagnosing CAD is the use of medical imaging, e.g., angiography. However, angiography is known for being costly and also associated with a number of side effects. Hence, the purpose of this study is to increase the accuracy of coronary heart disease diagnosis through selecting significant predictive features in order of their ranking. In this study, we propose an integrated method using machine learning. The machine learning methods of random trees (RTs), decision tree of C5.0, support vector machine (SVM), and decision tree of Chi-squared automatic interaction detection (CHAID) are used in this study. The proposed method shows promising results and the study confirms that the RTs model outperforms other models. View Full-Text
Keywords: heart disease diagnosis; coronary artery disease; machine learning; health informatics; data science; big data; predictive model; ensemble model; random forest; industry 4.0 heart disease diagnosis; coronary artery disease; machine learning; health informatics; data science; big data; predictive model; ensemble model; random forest; industry 4.0
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Joloudari, J.H.; Hassannataj Joloudari, E.; Saadatfar, H.; Ghasemigol, M.; Razavi, S.M.; Mosavi, A.; Nabipour, N.; Shamshirband, S.; Nadai, L. Coronary Artery Disease Diagnosis; Ranking the Significant Features Using a Random Trees Model. Int. J. Environ. Res. Public Health 2020, 17, 731.

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