AI models must be effective for diabetes risk prediction, but present techniques converge prematurely and lack transparency. This study proposes the DiaMetaHybridOptimizer (DMHO), an improved multi-phase adaptive hybrid framework to enhance prediction performance and clinical interpretability. DMHO utilizes a feedback-driven architecture with the Lemur Optimizer, Marine Predators Algorithm, and Manta Ray Foraging Optimization to maintain population diversity through fitness-ranked dynamic mutation. Based on five benchmark datasets and classifiers, DMHO exceeded baseline approaches with 96.8% accuracy. By reducing feature dimensionality by 75%, the framework identified compact subsets of clinically significant predictors including HbA1c, Glucose, and BMI. ANOVA analysis showed significant improvements in convergence and efficiency . DMHO's interpretable solution supports AI-assisted decision-making by combining computational outputs with real-world diagnostic reasoning. • DMHO combines multiple metaheuristic algorithms to enhance diabetes feature selection. • Adaptive mechanisms dynamically balance exploration and exploitation during optimization. • Multi-phase learning improves convergence and avoids premature stagnation. • The model achieves high prediction accuracy across multiple diabetes datasets.
Alzubaidi et al. (2026) studied this question.