Machine learning-based prediction of dataset-defined myocardial infarction risk: A retrospective computational study for precision cardiovascular risk assessment.

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.