Cardiac ailment recognition using ML techniques in E-healthcare

Abstract

Heart ailments can take numerous forms, and they are frequently referred to as cardio vascular illnesses. These can range from heart rhythm problems to birth anomalies to blood vessel disorders. It has been the main cause of death worldwide for several decades. To recognize the illness early and properly manage, it is critical to discover a precise and trustworthy approach for automating the process. Processing massive amounts of data in the field of medical sciences necessitates the application of data science. Here we employ a range of machine learning approaches to examine enormous data sets and aid in the accurate prediction of cardiac diseases. This paper explores the supervised learning models of Naive Bayes, Support Vector Machine, K-Nearest Neighbors, Decision Tree, in order to provide a comparison investigation for the most effective method. When compared to other algorithms, K-Nearest Neighbor provides the best accuracy at 86.89%.