A Hybrid Diffusion-Augmented Ensemble and RAG-LLM Framework for Early Prediction of Type 2 Diabetes Mellitus

Abstract

One of the most prevalent metabolic disorders that poses challenge to healthcare systems due to its rising frequency and accelerating prevalence is Type 2 Diabetes Mellitus (T2DM) and consequently prediction and intelligent management is necessary to forbid the emergence and progression of disease at an early stage. The disordered physiology of T2DM is characterized by the interrelated coupling of genetic predisposition, clinical characteristics, behavioral patterns, and environmental exposures and thus requires the implementation of advanced predictive modeling frameworks that support early disease identification, patient risk assessment, and individualized therapeutic interventions. Although accuracy and reliability in diabetic prediction and management have been enhanced through legacy technologies such as Artificial Intelligence (AI), exceptionally Machine Learning (ML) and Deep Learning (DL), they are still prone to limitations that compromise intelligent clinical decision support framework. To mitigate the identified challenges, the proposed conceptual framework includes AI-driven architecture that synergizes diffusion-based Generative Artificial Intelligence, ensemble techniques, Explainable Artificial Intelligence (XAI), and Retrieval-Augmented Generation (RAG)-enabled Large Language Models into a unified decision support system for early prediction and personalized management of T2DM.