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sklearn-pipeline

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Clinical-Diabetes-Risk-Assessment-with-Interpretable-Machine-Learning

An interpretable ML system for diabetes risk prediction using clinical data. Features SHAP explanations, model comparison (Random Forest vs XGBoost), and a deployment-ready pipeline. Achieves 0.85 AUC with clinical decision support.

  • Updated Jul 7, 2025
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