Optimization of Type 2 Diabetes Dietary Intervention Strategies Based on Large-Scale Interpretable Machine Learning
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Abstract
To address the lack of individualized adaptation in dietary intervention plans for type 2 diabetes, this study develops an intelligent dietary recommendation system based on XGBoost and reinforcement learning (RL), using combined dietary and metabolic data from 120,000 patients. SHAP (SHapley Additive exPlanations) was applied to perform both global and local feature interpretation, systematically evaluating the effects of carbohydrate intake frequency, fat types, and dietary diversity on changes in HbA1c levels. The overall model interpretability (R²) reaches 64%. A paired-sample t-test shows that the difference is statistically significant (P < 0.001). After six months of follow-up, the group following the optimized dietary suggestions shows an average HbA1c reduction of 1.5%, which is significantly greater than the 0.9% reduction in the group receiving routine guidance (independent-sample t-test, P = 0.003). The results suggest that the combination of interpretable machine learning and reinforcement learning can optimize dietary intervention strategies for type 2 diabetes and significantly improve blood glucose control, with promising potential for practical application.
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