Predicting Coronary Heart Disease: A Comparison between Machine Learning Models

Abstract

Coronary Heart Disease (CHD) is most important reasons of death all around the world.An early recognition of this disease may help to reduce the death rate. This paper uses Machine Learning (ML) techniques on the pastmedical data to forecast CHD. This paperapplies and compares three Classification algorithms – Logistic Regression (LR), K Nearest Neighbors (KNN) and Decision Tree (DT). These ML techniques are validated with K Fold cross validationmodel to improve the correctness of the models. The results of performance evaluation metricsshowed that Decision Tree is performing better than the other two models.