Adaptive Ensemble Selection for Personalized Cardiovascular Disease Prediction Using Clustering and Feature Selection

Cardiovascular disease (CVD) remains one of the leading causes of mortality worldwide, highlighting the need for early and precise prediction to support timely intervention. This study introduces an ensemble-based adaptive approach that personalizes CVD prediction by dynamically adjusting model configurations based on patient subgroups. To achieve this, various clustering techniques, including KMeans, DBSCAN, and MeanShift, are employed alongside feature selection methods such as chi-square, Mutual Information, and a baseline that incorporates all features. By tailoring classifier selection to each cluster, the proposed approach optimizes predictive performance, with ensemble models configured using Multi-Layer Perceptron (MLP) or Decision Tree classifiers. Through extensive experiments utilizing 10-fold cross-validation, results indicate that the adaptive ensemble consistently surpasses the static ensemble in key performance metrics, including accuracy, precision, recall, F1 score and AUC. In particular, the highest accuracy of 95.57%was achieved using MeanShift clustering with the entire set of features, demonstrating the effectiveness of density-based clustering in improving classification performance. Notably, this accuracy exceeds the best-reported results in previous studies, establishing a new benchmark for CVD prediction. These findings highlight the potential of adaptive ensemble selection to significantly improve diagnostic precision, providing valuable insights for personalized CVD prediction and broader applications in medical decision making.