Empirical analysis of cardiovascular diseases using machine learning and soft computing techniques

Abstract

Cardiovascular diseases are a one of the most exigent issue in healthcare domain. There have been various multidisciplinary approaches proposed and applied to reduce the mortality rate. As per literature and current study machine learning and soft computing techniques are efficient and widely accepted approaches in research community. This paper identifies and compares the various techniques of machine learning using Random Forest (RF), Support Vector Machine (SVM), XGBoost and Artificial Neural Network (ANN) and uncovers the F1 score, recall, precision to predict efficient and more accurate result. The results are further compared with existing benchmark models and showed significant improvement in heart disease prediction of patient.