Synergizing Clinical and Behavioral Data: A Review on Hybrid Machine Learning Models for Early Diabetes Detection.

Diabetes mellitus has emerged as one of the most rapidly increasing non-communicable diseases worldwide, making early detection a critical component of preventive healthcare. Traditional diagnostic methods rely heavily on clinical measurements, which often identify risk only after significant physiological changes have occurred. Recent advancements in machine learning have enabled automated prediction systems, yet single-model approaches frequently struggle with limited generalization, noisy data, and heterogeneous feature distributions. This review examines the evolution, design principles, and performance characteristics of hybrid machine learning models developed for early diabetes prediction using an integrated set of lifestyle and medical parameters. By synthesizing findings from recent studies, the review highlights how feature-selection techniques, ensemble classifiers, and multi-stage learning architectures improve predictive accuracy, robustness, and interpretability. The paper also analyzes commonly used datasets, class imbalance issues, parameter fusion strategies, and evaluation metrics applied across literature. Key observations indicate that combining behavioral patterns—such as physical activity, dietary habits, sleep cycles, and stress levels—with clinical attributes like glucose levels, insulin response, BMI, and blood pressure significantly enhances prediction capability. Finally, the review outlines research gaps, including the scarcity of real-time datasets, limited availability of population-specific lifestyle records, and the need for explainable hybrid frameworks suitable for deployment in resource-constrained environments. Overall, hybrid machine learning remains a promising pathway toward achieving reliable and early diabetes risk assessment, supporting more proactive and personalized healthcare systems.