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

Javad Hassannataj Joloudari, Edris Hassannataj Joloudari, Hamid Saadatfar, Mohammad Ghasemigol, Seyyed Mohammad Razavi, Amir Mosavi, Narjes Nabipour, Shahaboddin Shamshirband and Laszlo Nadai
Additional contact information
Javad Hassannataj Joloudari: Department of Computer Engineering, Faculty of Engineering, University of Birjand, Birjand 97175/615, Iran
Edris Hassannataj Joloudari: Department of Nursing, School of Nursing and Allied Medical Sciences, Maragheh Faculty of Medical Sciences, Maragheh, Iran
Hamid Saadatfar: Department of Computer Engineering, Faculty of Engineering, University of Birjand, Birjand 97175/615, Iran
Mohammad Ghasemigol: Department of Computer Engineering, Faculty of Engineering, University of Birjand, Birjand 97175/615, Iran
Seyyed Mohammad Razavi: Department of Electronics, Faculty of Electrical and Computer Engineering, University of Birjand, Birjand 9717434765, Iran
Amir Mosavi: Kalman Kando Faculty of Electrical Engineering, Obuda University, 1034 Budapest, Hungary
Narjes Nabipour: Department Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
Shahaboddin Shamshirband: Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam
Laszlo Nadai: Kalman Kando Faculty of Electrical Engineering, Obuda University, 1034 Budapest, Hungary

IJERPH, 2020, vol. 17, issue 3, 1-24

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 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.

Keywords: heart disease diagnosis; coronary artery disease; machine learning; health informatics; data science; big data; predictive model; ensemble model; random forest; industry 4.0 (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/1660-4601/17/3/731/pdf (application/pdf)
https://www.mdpi.com/1660-4601/17/3/731/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:17:y:2020:i:3:p:731-:d:312312

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().