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All Journal ELINVO (Electronics, Informatics, and Vocational Education)
de Deus Guterres, Jonas
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Computer Science & IT Education Electrical & Electronics Engineering

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A System Identification of Diabetes Based on Ensemble Method: Bagging, Random Forest, and Extreme Gradient Boosting de Deus Guterres, Jonas; Arifin, Fatchul
Elinvo (Electronics, Informatics, and Vocational Education) Vol. 10 No. 2 (2025): November 2025 (In-Press)
Publisher : Department of Electronic and Informatic Engineering Education, Faculty of Engineering, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/elinvo.v10i2.89649

Abstract

Diabetes is a prevalent chronic illness that is recognized worldwide, with an estimated prevalence in adults ranging from 42% to 170% globally. To reduce the likelihood of developing diabetes, it is vital for individuals at an increased risk to understand the importance of embracing healthy lifestyles and managing their consumption of foods that can potentially raise insulin levels in the body. Therefore, it is crucial to detect early pre-symptoms to minimize the incidence of individuals being afflicted by this condition without their awareness. Machine learning has emerged as a contemporary tool that aids in the prediction of various diseases, including diabetes, by analyzing patient data. Despite numerous research attempts using various machine learning techniques, achieving high accuracy in predicting diabetes has remained challenging. Therefore, this study implemented an ensemble approach that combined bagging, random forest, and Extreme Gradient Boost (XGBoost) algorithms to enhance the predictive performance for diabetes. This approach involved evaluating selected features based on their highest correlation and incorporating all available features in the analysis. Based on the results, the bagging technique demonstrated the highest accuracy of 0.83 in predicting model 6. Following closely behind was the random forest algorithm, which achieved an accuracy of 0.82, and XGBoost with an accuracy of 0.81.
Co-Authors Fatchul Arifin