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JAIT 2023 Vol.14(1): 66-76
doi: 10.12720/jait.14.1.66-76

An Optimized Machine Learning Approach for Coronary Artery Disease Detection

Savita 1, Geeta Rani 2,*, and Apeksha Mittal 3
1. Department of Computer Science, GD Goenka University, Gurgaon, India
2. Department of Computer and Communication Engineering, Manipal University Jaipur, Jaipur, India
3. Department of Engineering and Sciences, GD Goenka University, Gurgaon, India
*Correspondence: geetachhikara@gmail.com

Manuscript received December 14, 2021; revised July 9, 2022; accepted July 12, 2022; published February 14, 2023.

Abstract—Rising number of fatalities caused by Coronary Artery Disease is a major concern for the public as well as the health industry. Furthermore, diagnostic methods like angiography are expensive and unaffordable for those who are not well-off. Also, angiography is bothersome for the patient due to allergic responses, renal damage, and bleeding where the catheter is inserted. The researchers in literature proposed the machine learning-based approaches for the detection of Coronary Artery Disease. But, these techniques have low accuracy. Thus, there is a scope for optimization of these techniques. The objective of this paper is to develop a machine learning system for the early detection of Coronary Artery Disease early. Also, it employs optimization methods viz. Particle Swarm Optimization, and Firefly Algorithm with Principle Component Analysis based feature extraction and decision tree-based classification. The proposed technique reports an accuracy of 95.3%. Thus, the technological solution may be used as an automatic diagnostic aid.  
Keywords—CAD, data engineering, machine learning, medical diagnosis 

Cite: Savita, Geeta Rani, and Apeksha Mittal, "An Optimized Machine Learning Approach for Coronary Artery Disease Detection," Journal of Advances in Information Technology, Vol. 14, No. 1, pp. 66-76, February 2023.

Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.