CAD-CDN: Coronary Artery Disease Prediction Using Convolutional Neural Network with Modified Densenet

Abstract

Atherosclerosis is a synonym for coronary artery disease (CAD), a non-communicable cardiovascular disease. Coronary artery disease, cancer, and tumour illness pose significant human risks. Predicting coronary artery disease (CAD) is a difficult and time-consuming task in the medical field. Early prediction is a virtuoso skill in the medical area, particularly in the cardiovascular sector. Prior research on developing early prediction models provided a grasp of modern strategies for detecting variance in medical imaging. Cardiovascular disease prevention may be accomplished with a diet plan established by the concerned physician after early diagnosis. We proposed a CAD-CDN framework for coronary artery disease prediction using a Convolutional neural network (CNN) with modified densenet. The datasets are collected from the Kaggle repository, and the data normalization has been done with Affinity propagation with an adaptive damping factor (APADF). The best features are selected using ACO with SA as the Hybrid method. Finally, the classification was done with CNN with modified Densenet.  The experimental result has been done with various existing algorithms and proposed one. And the results have shown performance indicators including accuracy, precision, sensitivity, specificity, and measure value.