Explainable Machine Learning for Diabetes Risk Prediction
Transparent AI models that provide clear, human-understandable insights for early diabetes risk detection.
Overview
This research develops machine learning models for Type-2 diabetes prediction that are both accurate and explainable, enabling clinicians and individuals to understand and trust AI-driven decisions.
?The Challenge
Diabetes management is a daily challenge that affects lifestyle, diet, and long-term wellbeing. Many AI models achieve strong accuracy but operate as black boxes, limiting their use in healthcare where trust and clarity are essential.
✓Our Solution
We designed interpretable models supported by explainability techniques such as feature importance and LIME. These provide clear reasoning behind predictions, helping clinicians and patients make informed decisions.
Key Features
Results & Impact
- 1Published in IEEE IMCOM 2026
- 2High predictive performance with strong accuracy
- 3Improved trust in AI-driven predictions
- 4Supports safer clinical decision-making
Technical Approach
Models tested include Random Forest, XGBoost, LightGBM, and CatBoost. Explainability integrated using LIME and feature importance, ensuring alignment with clinical reasoning.
Future Directions
Focus on adaptive explanations and real-world clinical validation for deployment.
Technologies Used
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