Advancing Cardiovascular Risk Prediction: A Review of Machine Learning Models and Their Clinical Potential
DOI:
https://doi.org/10.18196/jet.v8i2.25208Keywords:
Machine Learning, Heart Disease Risk Prediction, Clinical Applications, Predictive Modeling, Early DetectionAbstract
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.References
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