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All Journal Engineering, Mathematics and Computer Science Journal (EMACS)
Januario, Albertus
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Industrial & Manufacturing Engineering Mathematics

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Journal : Engineering, Mathematics and Computer Science Journal (EMACS)

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A Comparative Study of Machine Learning and Stacking Ensemble Models for Diabetes Prediction Jabar, Bakti Amirul; Januario, Albertus; Sanjaya, Davin Miguel; Tanuwijaya, James
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 8 No. 1 (2026): EMACS (In Press)
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v8i1.15332

Abstract

Diabetes is a chronic metabolic disease and increasingly widespread disease around the world and early diagnosis is crucial. Methodology In this study, the performance of three machine learning models (Logistic- Regression, K-Nearest Neighbour (KNN) and Naive Bayes) is reviewed under the task of diabetes classification using Pima Indians Diabetes Dataset. To tackle the class imbalance, we applied imputation, SMOTE for the data pre-processing, and Min-Max Scaling to enhance the prediction performance. Further, we have applied the ensemble learning and stacking, where all the three models have been used as meta classifiers. The results indicate that KNN had the best individual model performance (accuracy 77.27%, AUC 0.8444%) but the stacking ensemble with meta-model being Logistic Regression is superior to any model (accuracy 80.52%, AUC 0.8604%). This suggests that ensemble learning can also improve the accuracy of diabetes diagnosis. These findings demonstrate that combining multiple classification approaches may provide more stable predictions across different patient conditions and clinical attributes In addition the preprocessing stages contributed to reducing noise and improving data consistency before model training The study also highlights the potential use of ensemble-based systems in supporting healthcare professionals during preliminary diabetes screening particularly in environments with limited medical resources and increasing numbers of diabetes cases requiring rapid assessment.
Co-Authors Jabar, Bakti Amirul Sanjaya, Davin Miguel Tanuwijaya, James