Gallstone Status Classification Using Adaptive Performance-Diversity Hybrid Ensemble with Pareto-Based Multi-Objective Selection (APDHE-Pareto)

Background/Objectives: Machine-learning models may support gallstone status classification using heterogeneous clinical and body-composition variables; however, ensemble methods often rely on predefined model combinations or arbitrarily weighted performance criteria. This study proposes the Adaptive Performance-Diversity Hybrid Ensemble with Pareto-Based Multi-Objective Selection (APDHE-Pareto), a leakage-controlled multi-objective ensemble framework that integrates complementary performance measures, prediction diversity, and cross-validation stability. Methods: An open-access dataset comprising 319 observations and 38 predictors was divided into a development set (n = 255) and a held-out internal test set (n = 64). Candidate classifiers were evaluated using Pareto non-dominated sorting across ROC-AUC, balanced accuracy, F1-score, and Matthews correlation coefficient. Hyperparameter optimization, probability calibration, ensemble construction, and threshold optimization were performed exclusively within the development process. Performance was assessed using repeated 10-fold nested stratified cross-validation (100 outer evaluations) and a one-time evaluation on the held-out internal test set. Results: Among the primary model comparisons, APDHE-Pareto achieved the highest mean ROC-AUC during repeated nested cross-validation (0.859). On the held-out internal test set, it achieved the highest ROC-AUC (0.910), balanced accuracy (0.828), F1-score (0.814), Matthews correlation coefficient (0.664), and the lowest Brier score (0.131) among the evaluated models. Formal paired comparison with the prespecified Extra Trees baseline showed no statistically significant difference in classification errors or ROC-AUC. Conclusions: APDHE-Pareto provides a systematic and leakage-controlled framework for multi-objective ensemble selection while achieving competitive predictive performance. Independent external validation is required to confirm its generalizability and potential clinical utility.