[Online]Early Prediction of Diabetes Using a Stacking Ensemble of Tree-Based Classifiers

Early Prediction of Diabetes Using a Stacking Ensemble of Tree-Based Classifiers
ID:99 Submission ID:316 View Protection:ATTENDEE Updated Time:2025-12-23 13:10:49 Hits:366 Online

Start Time:2025-12-30 13:45 (Asia/Amman)

Duration:15min

Session:[S5] Track 5: Emerging Trends of AI/ML » [S5-2] Track 5: Emerging Trends of AI/ML

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Abstract
Early prediction of diabetes can significantly improve patient outcomes by enabling timely interventions. In this study, we propose a stacking ensemble model composed of four tree-based classifiers – Random Forest, XGBoost, LightGBM, and CatBoost – with a logistic regression meta-learner for the early prediction of diabetes. The model is trained and evaluated on the PIMA Indians Diabetes Dataset, using data preprocessing steps to handle missing values (zeros replaced with median imputation) and feature standardization. We perform an 80/20 stratified train-test split and tune the decision threshold. The stacking ensemble achieves superior performance compared to individual classifiers and prior ensemble approaches in literature. Key performance metrics include an accuracy and ROC-AUC of about 0.85 on the test set. These results improve upon the baseline non-ensemble methods (around 77% accuracy)​ and are competitive with state-of-the-art ensemble models such as AdaBoost and XGBoost. The proposed model and findings suggest that stacking heterogeneous tree-based learners is a promising approach for early diabetes detection.
 
Keywords
Diabetes Mellitus, Stacking Ensemble, Random Forest, XGBoost, LightGBM, CatBoost, Early Prediction, Machine Learning, Classification, ROC-AUC
Speaker
Waleed Alomoush
Associate Professor Plekhanov Russian University of Economics in Dubai.; Dubai Knowledge Park; Dubai; UAE

Submission Author
Waleed Alomoush Plekhanov Russian University of Economics in Dubai.; Dubai Knowledge Park; Dubai; UAE
Ayat Alrosan Dubai; UAE;Artificial Intelligence Center for Humanities and social science research; Alwasl University
Saeed Alsuwaidi Skyline University College
Fuad Alhosban Computer Science Department, College of Computing and Intelligent Systems, University of Al Dhaid, Sharjah, UAE
Mohanad A. Deif University of Sharjah
Zakaria Che muda INTI-IU University
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