Interaction-Enhanced and Explainable Machine Learning for Diabetes Risk Prediction

Diabetes mellitus is a common chronic metabolic disease for which early diagnosis is crucial for prevention and treatment. As the amount of structured clinical information grows, machine learning has emerged as a valuable instrument to predict diabetes risk; nevertheless, several studies demonstrate the application of machine learning models to diabetes risk prediction based on original clinical features, while highlighting a general lack of systematic inspection of feature combinations and model interpretability. Various machine learning models have been built and tested in this research to predict diabetes risk using structured clinical data. Clinically motivated interaction terms were built to capture nonlinear physiological relationships, and a two-criterion selection approach using tree-based split gain and SHAP importance was used to identify meaningful interactions. An interaction-enhanced XGBoost model was then trained and compared with baseline and complete-interaction models using conventional classification metrics. The results of the experiment indicate that the noise created by indiscriminate inclusion of interaction features can lead to degraded generalization performance and that selectively retained interactions can increase sensitivity without compromising the discriminative performance. Glucose, BMI, and age were also identified as dominant predictors and verified in diabetes prediction through feature ablation analysis. Furthermore, the SHAP interpretability analysis presented clear and clinically coherent model behavior explanations. Overall, the developed framework implies that there is a sensible trade-off between predictive efficiency and interpretability, which highlights the importance of focused feature interaction modeling of reliable predictive and explainable diabetes risk assessments.