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Submitted
May 30, 2026
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July 3, 2026
Published
July 12, 2026
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Abstract

This study aims to develop a machine learning-based diabetes risk prediction model using the ML.NET framework. The dataset utilized is a balanced-split version of the 2015 BRFSS, consisting of 70,692 respondents and 21 health indicator variables. Two training approaches were applied to analyze model performance: a baseline with default parameters and hyperparameter tuning. The preprocessing stage involved combining variables into feature vectors, Min-Max normalization, and an 80:20 train-test data split. The models were trained using four algorithms: SDCA Logistic Regression, LBFGS Logistic Regression, LightGBM, and FastTree. Evaluation results showed that LightGBM with the hyperparameter tuning approach, delivered the most consistent performance, achieving 75.37% accuracy, 82.86% AUC, 76.22% F1-score, 72.92% precision, and 79.83% recall. Feature analysis confirmed that GenHlth, HighBP, BMI, HighChol, and Age contributed dominantly to diabetes risk, aligning with medical literature regarding metabolic factors. The best-performing LightGBM model was then integrated into a .NET-based prototype application with a Razor Pages web interface. The practical contribution of this research is proof of concept for machine learning integration into e-health systems to support early detection and digital prevention of diabetes complications in the future.

Keywords

Diabetes LightGBM Logistic Regression Machine Learning ML.NET

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Copyright (c) 2026 Resi Taufan, Fahmi Ardiansyah, Annisa Elfina Augustia

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This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

How to Cite
Taufan, R., Ardiansyah, F. ., & Augustia, A. E. . (2026). Evaluation of SDCA, LBFGS, LightGBM and FastTree in ML.NET for Diabetes Prediction. Jurnal Sains, Nalar, Dan Aplikasi Teknologi Informasi, 5(2), 97–107. https://doi.org/10.20885/snati.v5.i2.49517
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