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

• URL: http://arxiv.org/abs/2001.09841v1
• Date: Thu, 16 Jan 2020 20:01:09 GMT
• Title: Coronary Artery Disease Diagnosis; Ranking the Significant Features Using Random Trees Model
• Authors: Javad Hassannataj Joloudari, Edris Hassannataj Joloudari, Hamid Saadatfar, Mohammad GhasemiGol, Seyyed Mohammad Razavi, Amir Mosavi, Narjes Nabipour, Shahaboddin Shamshirband, and Laszlo Nadai
• Abstract summary: 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. The proposed method shows promising results and the study confirms that RTs model outperforms other models.
• Score: 0.9634136878988853
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Heart disease is one of the most common diseases in middle-aged citizens. Among the vast number of heart diseases, the 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), 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 RTs model outperforms other models.

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