An Explainable AI Framework for Online Diabetes Risk Prediction with a Personalized Chatbot Assistant
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".