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

International Journal of Environmental Research and Public Health

Narjes Nabipour¹  Shahaboddin Shamshirband²  Hamid Saadatfar³  JavadHassannataj Joloudari³  Mohammad Ghasemigol³  SeyyedMohammad Razavi⁴  Edris Hassannataj Joloudari⁵  Amir Mosavi⁶  Laszlo Nadai⁶ 

[1] Department Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam;Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam;Department of Computer Engineering, Faculty of Engineering, University of Birjand, Birjand 97175/615, Iran;Department of Electronics, Faculty of Electrical and Computer Engineering, University of Birjand, Birjand 9717434765, Iran;Department of Nursing, School of Nursing and Allied Medical Sciences, Maragheh Faculty of Medical Sciences, Maragheh, Iran;Kalman Kando Faculty of Electrical Engineering, Obuda University, 1034 Budapest, Hungary;

关键词: heart disease diagnosis;    coronary artery disease;    machine learning;    health informatics;    data science;    big data;    predictive model;    ensemble model;    random forest;    industry 4.0;   

DOI  :  10.3390/ijerph17030731

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown