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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Main Authors | , , , , , |
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Format | Journal Article |
Language | English |
Published |
21.09.2024
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Subjects | |
Online Access | Get full text |
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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%. |
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DOI: | 10.48550/arxiv.2409.14231 |