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
· International Journal of Adv... · 0 citations