Cardiovascular Disease Prediction using ML Techniques to Find the Best Accurate Model

Abstract

Heart disease and cardiovascular disease are both illnesses that damage the heart. Correct functioning of the heart is very important for physical health. There are numerous types of cardio disease includes myocardial ischemia, cardiac arrest, congenital heart disease, myocardial infarction, peripheral heart disease, coronary heart disease, HCD. These types of diseases can be life threatening. It has been diagnosed that men experience more HCD compared to women. Also, it has been said that men diagnosed quick heart attacks than in women. It is the need to be early diagnosis of these diseases more efficiently and accurately. The goal of the study is to create and evaluate several ML algorithms for reliably predicting if a patient will have a cardiovascular ailment. The primary contribution is the evaluation of three well-known ML algorithms, namely Random Forest, Naive Bayes and Support Vector Machines, for predicting cardiovascular disease. When compared with all the results of various algorithms, Random Forest has the highest accuracy (97.94%).