Predicting Coronary Heart Disease Using a Suite of Machine Learning Models
This work addresses early diagnosis of coronary heart disease for healthcare applications, but it is incremental as it benchmarks existing methods without introducing new techniques.
The study tackled the problem of predicting coronary heart disease by applying several machine learning models, finding that Random Forest with oversampling achieved the highest accuracy of 84%.
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%.