Noise Removal Using Adaptive Damping Based Affinity Propagation Clustering Algorithm in Coronary Artery Disease

Abstract

The health business generates enormous amounts of data, and by using this massive quantity of data, many illnesses may be recognized, predicted, and even treated at early stage. Humans face significant danger from coronary artery disease, cancer, and tumor illness. Predicting Coronary Artery Disease (CAD) is a difficult and time-consuming process in the medical sector. Early prediction is a virtuoso skill in the medical area, particularly in the cardiovascular sector. Prior research on developing early prediction model 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. This study aims to forecast CAD utilizing the suggested approach by creating noise reduction in CAD using the Adaptive Damping based Affinity Propagation (ADAP) clustering algorithm. This kind of knowledge-based identification is critical for accurate prediction. Despite the lack of supporting evidence, this substantial strategy positively influences determining variance in medical disciplines. Additionally, this publication has minimized the use of noisy data to aid in illness identification. This article discusses novel adaptive image-based clustering algorithms and compares them to established classification methods for predicting CAD early and with better accuracy.