Comparison of Assembly Methods in Machine Learning for the Early Prediction of Acute Myocardial Infarction

Cardiovascular disease (CVD) is one of the leading causes of mortality worldwide, which raises the fundamental need to apply efficient predictive tools to support clinical deci-sion-making. This study compares the predictive performance of Bagging, Random Forest, Extra Trees, Gradient Boosting, and AdaBoost ensemble learning algorithms applied to a clinical dataset of CVD patients. The methodology included data preprocessing and cross-validation to regulate generalization. Performance was evaluated using multiple metrics: accuracy, F1 score, precision, recall, Cohen's Kappa, and area under the curve (AUC). Among the models evaluated, Bagging demonstrated the best overall performance (accuracy: 93.36 %± 0.22; F1 Score: 0.936; AUC: 0.9686), reaching also the lowest average rank (1.0) in Friedman's test and placing, together with Extra Trees (90.76 %± 0.18; AUC: 0.9689), in the superior statistical group (group A) according to Nemenyi's post-hoc test. Both models evidenced high agreement with the real labels (Kappa: 0.87 and 0.83, respec-tively), which reinforces their reliability in real clinical settings. The results validate the superiority of aggregation-based ensemble methods in terms of accuracy, stability, and concordance, highlighting Bagging and Extra Trees as preferred candidates for cardio-vascular diagnostic support systems, where reliability and generalization are essential.