Open access
Aug 2026
An XGBoost-based predictive framework for diabetes mellitus multi-classification
benchmarking against Logistic Regression, Random Forest, Support Vector Machine, Decision Tree, and K-Nearest Neighbors showed that XGBoost provided the strongest performance, highlighting the effectiveness of XGBoost while emphasizing classification accuracy in multiclass diabetes prediction systems.
M. M. El Sherbiny, M. G. Abdel-Fattah, Ali E. Takieldeen et al.
· Scientific Reports · 1 citation