Title: Early diagnosis of coronary artery disease by SVM, decision tree algorithms and ensemble methods

Authors: Marziye Narangifard; Hooman Tahayori; Hamid Reza Ghaedsharaf; Mehrdad Tirandazian

Addresses: Department of Computer Science and Engineering and IT, Shiraz University, Shiraz, Iran ' Department of Computer Science and Engineering and IT, Shiraz University, Shiraz, Iran ' Department of Network Science and Technology, University of Tehran, Tehran, Iran ' Department of Computer Science, Ryerson University, Toronto, Canada

Abstract: Heart diseases are one of the main causes of death around the world. The most reliable method for heart disease diagnosis is angiography, which is costly, invasive and has the risk of death. This study applies variations of decision tree (DT), support vector machine (SVM) and voting algorithms to construct a heart disease diagnosis predictive model. We show that integrating medical knowledge and statistical knowledge as well as fine tuning the parameters of the used models lead to more effective heart disease diagnosis models. We use two methods for implementing the proposed model. The obtained results in both methods show that voting algorithm and random forest outperform other methods. Moreover, the achieved accuracies show improvements over other existing methods.

Keywords: data mining; machine learning; decision tree; support vector machine; SVM; voting; random forest; forest PA; heart disease; UCI dataset.

DOI: 10.1504/IJMEI.2022.123921

International Journal of Medical Engineering and Informatics, 2022 Vol.14 No.4, pp.295 - 305

Received: 18 Apr 2020
Accepted: 12 Aug 2020
Published online: 05 Jul 2022 *

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