AI-Driven Glucose Forecasting and Personalized Feedback for Diabetic Patients via a Mobile Health Platform

Diabetes is a serious chronic condition affecting over 9% of the global adult population. Poor management of the disease can lead to severe complications such as neuropathy and retinopathy. In recent years, with the emergence of technological means, several schemes have been proposed to foster more efficient disease management. However, many existing solutions lack real-time forecasting or adaptive personalization. In addressing the current limitations, this paper proposes a lightweight, AI-driven mobile platform that integrates glucose level prediction and personalized recommendation generation. The system employs third-order polynomial regression trained on real-world glucose data from the Ohio Type 1 Diabetes dataset to forecast short-term glucose trends based on historical user inputs and uses a large language model to generate personalized recommendations. In urgent cases, the system can trigger location-based alerts to notify nearby users or emergency contacts. The proposed regression model achieved an average mean absolute error of $\mathbf{5. 0 8}$ milligrams per deciliter, a root mean square error of 8.21 milligrams per deciliter, and a coefficient of determination of $\mathbf{0. 9 7 6}$. In addition, qualitative testing showed that the language model generated recommendations that aligned with specific user profiles. Overall, the results demonstrated in this paper highlight the system’s potential to support personalized diabetes self-management through a unified and user-friendly mobile platform.