Preprint Article Version 1 This version is not peer-reviewed

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

Version 1 : Received: 16 January 2020 / Approved: 20 January 2020 / Online: 20 January 2020 (09:11:14 CET)

A peer-reviewed article of this Preprint also exists.

Joloudari, J.H.; Joloudari, E.H.; 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. Joloudari, J.H.; Joloudari, E.H.; 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.

Journal reference: Int. J. Environ. Res. Public Health 2020, 17, 731
DOI: 10.3390/ijerph17030731

Abstract

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 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 by 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), the decision tree of C5.0, support vector machine (SVM), the 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.

Subject Areas

heart disease; coronary artery disease; machine learning; deep learning; predictive features; coronary artery disease diagnosis; health informatics

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