Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Javad Hassannataj Joloudari
,
Edris Hassannataj Joloudari
,
Hamid Saadatfar
ORCID logo ,
Mohammad Ghasemigol
,
Seyyed Mohammad Razavi
,
Amir Mosavi
,
Narjes Nabipour
ORCID logo ,
Shahaboddin Shamshirband
* ORCID logo ,
Laszlo Nadai
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.

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.

Keywords

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

Subject

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Download PDF

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.

Leave a public comment
Send a private comment to the author(s)
* All users must log in before leaving a comment
Views 0
Downloads 0
Comments 0
Metrics 0
Get PDF Cite Share 0
Bookmark BibSonomy Mendeley Reddit Delicious
×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.