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All Journal Engineering, Mathematics and Computer Science Journal (EMACS) JGGAG (Journal of Games, Game Art, and Gamification)
Shiddiqi, Hafizh Ash
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Arts Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Industrial & Manufacturing Engineering Mathematics Other

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

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Leveraging Support Vector Machines and Ensemble Learning for Early Diabetes Risk Assessment: A Comparative Study Shiddiqi, Hafizh Ash; Setiawan, Karli Eka; Fredyan, Renaldy
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 7 No. 1 (2025): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v7i1.12846

Abstract

Currently, diabetes is a hidden, serious threat to human lifestyles through daily food and drink, which has become a formidable global health challenge. As a contribution, this study suggests a way to use machine learning to find people with diabetes by looking at certain health parameters. It does this by using different Support Vector Machine (SVM)-based models, such as different SVMs with different kernels, such as linear, polynomial, radial basis function, and sigmoid kernels; different ensemble bagging with SVM; and different ensemble stacking with various SVM models. The findings demonstrated that utilizing a single SVM model with a linear kernel, ensemble bagging with a linear SVM, and ensemble stacking with different SVM models yielded the most accurate results, achieving 95% accuracy in both diabetes presence and absence. This lends credence to the idea that the incorporation of a linear kernel has the potential to improve the accuracy of determining whether or not diabetic illness is present.
Co-Authors Ardiyanto, Elshad Ryan Fredyan, Renaldy Hermawan, Eric Savero Novika, Andien Dwi Pratama, Galih Dea Setiawan, Karli Eka