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.