Stability-Aware Hybrid Intelligence for Interpretable and Scalable Diabetes Risk Prediction

Abstract

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.

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