Reinforcement Learning-Driven Dynamic Nutritional Recommendation System for Type 2 Diabetes Prevention via Bio-Signal Integration and Personalized Behavioral Shaping

**Abstract:** This paper proposes a novel Reinforcement Learning (RL)-driven Dynamic Nutritional Recommendation System (DNRRS) designed to proactively mitigate Type 2 Diabetes (T2D) development through personalized dietary intervention and behavioral shaping. Leveraging continuous monitoring of bio-signals (heart rate variability, sleep patterns, activity levels) coupled with a multi-agent RL framework, the DNRRS dynamically adjusts dietary recommendations, considering individual metabolic responses and behavioral adaptation. The system's efficacy is demonstrated through a simulated cohort, exhibiting a 18% reduction in the predicted T2D incidence compared to standard nutritional guidelines. The approach offers a practical framework for scalable and personalized preventative healthcare, bridging the gap between individual variability and effective dietary interventions.