Predicting Coronary Heart Disease Using a Suite of Machine Learning Models

2024/09/21 by Jamal N. Al‐Karaki, Philip Ilono, Al-Karaki, Jamal +7
Health Professions · #Artificial Intelligence (cs.AI) #Artificial Intelligence in Healthcare #FOS: Computer and information sciences

paper · pdf · doi:10.48550/arxiv.2409.14231

openalex publication_date 2024/09/21 · openalex created_date 2024/10/26 · openalex updated_date 2026/07/28

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%.

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