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This paper proposes an Extreme Gradient Boosting (XGBoost)-based predictive framework for multi-classification of diabetes mellitus into non-diabetic, pre-diabetic, and diabetic classes. After standardization and exclusion of non-clinical identifiers, Duplicate clinical records were removed from the original dataset, leaving 826 unique records. Comprehensive preprocessing pipeline used a stratified 70:30 train-test split and five-fold cross-validation; scaling and resampling were performed only within training partitions. Experimental results on the original dataset XGBoost achieved an accuracy of 99.60%. Both Random Over Sampling (ROS) and Syntenic minority over sampling technique (SMOTE) have also achieved 99.60% accuracy but provided improved generalization at the expense of higher computational cost. In contrast, Random Under Sampling (RUS) and Cluster Centroids (CC) reduced accuracy to 92.74% and 90.32%, respectively due to information loss. Across five folds, the original XGBoost model achieved 98.79\u2009\u00b1\u20091.13% accuracy. Benchmarking against Logistic Regression, Random Forest, Support Vector Machine, Decision Tree, and K-Nearest Neighbors showed that XGBoost provided the strongest performance. These findings highlight the effectiveness of XGBoost while emphasizing classification accuracy in multiclass diabetes prediction systems.<\/jats:p>","DOI":"10.1038\/s41598-026-65844-3","type":"journal-article","created":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T19:13:05Z","timestamp":1786734785000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["An XGBoost-based predictive framework for diabetes mellitus multi-classification"],"prefix":"10.1038","volume":"16","author":[{"given":"Moataz M.","family":"El Sherbiny","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohamed G.","family":"Abdelfattah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali E.","family":"Takieldeen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hossam","family":"El-Din Mostafa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hala B.","family":"Nafea","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,14]]},"reference":[{"key":"65844_CR1","doi-asserted-by":"publisher","first-page":"2225","DOI":"10.1007\/s00125-024-06221-5","volume":"67","author":"S Du","year":"2024","unstructured":"Du, S. et al. 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