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Author

Cheikh Sarr

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Open access Aug 2026

A HYBRID ANN-XGBOOST FRAMEWORK FOR EARLY DIABETES RISK PREDICTION UNDER CLASS IMBALANCE: EXTERNAL CLINICAL VALIDATION IN AN AFRICAN HEALTHCARE SETTING

This study proposes a Hybrid ANN-XGBoost framework for early diabetes risk prediction under class-imbalanced conditions that combines the nonlinear feature representation capability of Artificial Neural Networks with the gradient boosting classification mechanism of XGBoost.

Djiwa Ntela Oga, P. Houngue, Cheikh Sarr · 0 citations

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