Predicting Coronary Heart Disease Using a Suite of Machine Learning Models

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 mac...

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Bibliographic Details
Published inarXiv.org
Main Authors Al-Karaki, Jamal, Ilono, Philip, Baweja, Sanchit, Naghiyev, Jalal, Raja Singh Yadav, Muhammad Al-Zafar Khan
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 21.09.2024
Subjects
Algorithms
Diagnosis
Heart diseases
Machine learning
Predictions
Supervised learning
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Summary: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%.
ISSN:2331-8422