Key result
Random Forest predicts myocardial infarction risk with ~97% accuracy, outperforming alternative machine learning models.
Why the study?
The study aimed to develop and evaluate machine learning and deep learning models for early prediction of myocardial infarction/heart disease risk and improve clinical interpretability and methodological transparency.
Effect estimate: Accuracy 97.0%, ROC-AUC 0.97
A Random Forest model achieved strong internal predictive performance (97.0% accuracy, 0.97 ROC-AUC) for classifying a dataset-defined surrogate of heart disease risk, though external validation in real-world clinical populations is required.
Should not yet change MI risk prediction practice; supports external validation of Random Forest models in prospective cohorts.
Objective: This study aimed to develop and evaluate machine learning and deep learning models for the early prediction of dataset-defined myocardial infarction/heart disease risk using structured demographic, clinical, electrocardiographic, exercise-testing, and angiographic-related variables. The study also aimed to improve the clinical interpretability and methodological transparency of artificial intelligence-based cardiovascular risk prediction. Methods: A retrospective computational modeling design was applied to a merged public heart disease dataset containing 1,888 records and 14 variables. The outcome was the original binary dataset label indicating higher versus lower likelihood of heart disease/heart attack risk. Data preprocessing included missing-value management, categorical encoding, normalization of continuous variables, and stratified model evaluation. Three models were compared: Random Forest, Support Vector Machine, and a multilayer perceptron deep neural network. Model performance was assessed using accuracy, precision/positive predictive value, recall/sensitivity, F1-score, ROC-AUC, and class-wise support. A separate statistical analysis section was added to report descriptive analysis, outcome distribution, feature correlation, and clinically relevant evaluation metrics. Results: The Random Forest model achieved the strongest overall performance, with 97.0% accuracy, 96.0%-97.0% class-wise precision, 96.0%-97.0% class-wise recall, 97.0% F1-score, and ROC-AUC of 0.97. The deep neural network achieved 92.3% accuracy and ROC-AUC of 0.94, whereas the Support Vector Machine achieved 87.0% accuracy and ROC-AUC of 0.87. The balanced distribution of the dataset supported internal model training but may not reflect the lower prevalence of myocardial infarction in real-world healthcare settings. Conclusion: The findings suggest that Random Forest can provide strong internal predictive performance for structured cardiovascular risk classification. However, the outcome represents a dataset-defined surrogate classification rather than independently adjudicated acute myocardial infarction. Therefore, external validation, calibration, comparison with validated clinical risk scores, and prospective evaluation in real-world clinical populations are required before clinical deployment.
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Al-Batah et al. (2026) studied Dataset-defined myocardial infarction/heart disease risk (n=1,888). Random Forest machine learning model vs. Support Vector Machine and Deep Neural Network models was evaluated on Prediction of dataset-defined myocardial infarction/heart disease risk (Accuracy and ROC-AUC) (Accuracy 97.0%, ROC-AUC 0.97). The Random Forest predictive model achieved 97.0% accuracy and a ROC-AUC of 0.97 for classifying dataset-defined myocardial infarction risk, outperforming the evaluated alternative models.
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