An Explainable AI Framework for Online Diabetes Risk Prediction with a Personalized Chatbot Assistant

Abstract

Background and Objective: Diabetes is a prevalent chronic disease that presents considerable health risks, making prompt diagnosis and treatment essential to avert complications. Traditional Artificial Intelligence (AI) models for diabetes prediction often operate as black boxes. A major issue caused by this is that black boxes lack interpretability, which impacts their effectiveness in clinical use cases. We introduce a novel online recommendation framework using explainable AI (XAI) to predict type II diabetes risk and provide clear, actionable analyses with a personalized chatbot assistant. Methods: To make the model, we chose the CatBoost classifier and SHapley Additive exPlanations (SHAP) due to their ability to provide accurate predictions. Using those tools, we analyzed 16 individual risk factors from a dataset of 520 patients. We applied the Synthetic Minority Over-sampling Technique (SMOTE) to reduce the effect of data imbalance. We also developed an interactive interface that allows users to input data, visualize personalized risk profiles, and understand the driving factors behind predictions. Finally, large language models (LLMs) were integrated into the interface for patient-specific recommendations for improving health and lifestyle through a personalized chatbot assistant. Results: The model demonstrated great predictive performance, with an Area Under the ROC Curve (AUC) of 0.99, a Cohen Kappa score of 0.978, and an F1 score of 0.99. For the minority class, SMOTE application improved performance metrics, resulting in an AUC of 0.98 and an F1 score of 0.91 for female patients. Conclusions: This study proposes an explainable AI framework for predicting diabetes risk online and providing patient-specific advice through a personalized chatbot assistant. This will help to facilitate better decision-making and improved management of diabetes risk.