Prediction of Cardiovascular Diseases Using Bio-Inspired MKCB Optimization Algorithm

Abstract

Early detection and prevention of cardiovascular diseases (CVDs) are crucial for reducing mortality and morbidity associated with these illnesses. Data mining techniques can play an important role in this effort by extracting useful information from large and complex datasets, such as electronic health records (EHRs) and clinical trial data, to identify risk factors for CVDs, predict the development of these diseases, and improve the diagnosis and treatment of patients. Heart disease is one of the most common causes of mortality in the world today. While attempting to predict cardiovascular disease, clinical data analysis faces major challenges. It has been shown that it is possible to draw inferences and make predictions from the large amount of data produced by the healthcare industry with the aid of machine learning (ML). In addition, we have seen the use of ML techniques in recent developments in a number of IoT fields (IoT). Using ML to predict heart disease has only been the subject of a few studies. In this article, we propose a novel multi-Kernel optimization technique with cat boost algorithm to predict the cardiovascular disease by detecting essential features using machine learning techniques.