Predicting Coronary Heart Disease Using a Suite of Machine Learning Models

TL;DR

This paper evaluates various machine learning models for predicting coronary heart disease, highlighting that Random Forest with oversampling achieves the highest accuracy of 84%, offering a low-cost, non-invasive diagnostic alternative.

Contribution

The study benchmarks multiple machine learning algorithms for heart disease prediction, identifying the most accurate model and demonstrating the effectiveness of oversampling techniques.

Findings

Random Forest achieved 84% accuracy.

Oversampling improved model performance.

Machine learning offers a non-invasive diagnostic tool.

Abstract

Coronary Heart Disease affects millions of people worldwide and is a well-studied area of healthcare. There are many viable and accurate methods for the diagnosis and prediction of heart disease, but they have limiting points such as invasiveness, late detection, or cost. Supervised learning via machine learning algorithms presents a low-cost (computationally speaking), non-invasive solution that can be a precursor for early diagnosis. In this study, we applied several well-known methods and benchmarked their performance against each other. It was found that Random Forest with oversampling of the predictor variable produced the highest accuracy of 84%.