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Regen, Rona
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Library & Information Science

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Advancing Cardiovascular Risk Prediction: A Review of Machine Learning Models and Their Clinical Potential Regen, Rona; Setiawan, Hendra
Journal of Electrical Technology UMY Vol. 8 No. 2 (2024): December
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jet.v8i2.25208

Abstract

This study conducts a systematic literature review on the application of machine learning technology in predicting heart disease risk. A total of 20 recent articles were identified and analyzed to evaluate the most used algorithms and their performance. The results show that Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), and K-Nearest Neighbors (KNN) are the most frequently applied models, with average accuracies of 89.56%, 83.14%, 83.14%, 82.57%, and 79.40%, respectively. In addition to comparing accuracy, this review also evaluates the strengths, weaknesses, and potential challenges of implementing each algorithm in clinical applications. The analysis reveals that Random Forest demonstrates high stability and accuracy, making it the leading candidate for large-scale clinical heart disease risk prediction applications. These findings are expected to provide new insights for the development of more accurate, reliable, and clinically deployable machine learning predictive models to support medical decision-making.
Co-Authors Hendra Setiawan