An Optimized Machine Learning Approach for Coronary Artery Disease Detection

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.