Machine Learning Framework for Diabetes Risk Stratification & Complication Prediction
Predicting long-term risks and complications to enable proactive and preventive care.
Overview
This research presents a machine learning framework for identifying diabetes risk levels and predicting potential complications, supporting early intervention strategies.
?The Challenge
Diabetes complications often develop gradually and are detected too late. Traditional systems focus on monitoring rather than predicting future risks, limiting preventive care.
✓Our Solution
We built a predictive framework combining supervised learning and clustering techniques to stratify patients by risk and identify patterns linked to complications.
Key Features
Results & Impact
- 1Accepted in IEEE ICAIII 2026
- 2Improved early risk identification
- 3Enabled proactive intervention strategies
- 4Supports preventive healthcare models
Technical Approach
Combination of supervised learning models and unsupervised clustering for patient segmentation. Techniques include class imbalance handling and clinical dataset benchmarking.
Future Directions
Integration with real-time patient data and deployment in preventive health platforms like SugarCare.
Technologies Used
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