An Effectual Machine Learning Based Coronary Artery Disease Classification for Low Error Rates

Abstract

Heart syndrome is the common and significant reasonfordeath in the world nowadays. Estimation of cardiovascular infection is a critical experiment in the region of clinical data breakdown. Machine knowledge has been presented to be operative in supportof making conclusions and estimations from the large number of recordscreated by healthcare engineering. The prediction model is familiarized with diversegroupings of structures and several well-known classification practices. The proposed approach deals with the efficient machine learning model for the detection of the CAD which are having low validation and testing errors and achieves high true positive error rates.Our proposed model consists of hybridization of optimization processes using PSO and firefly nature-inspired and the classification is performed on the data using discriminant analysis.The proposed approach is achieving above 95% accuracy of the detection on different test samples to achieve high-performance classifications.