Construction of a Diabetes Prediction Model Based on Machine Learning

Ke Peng

doi:10.54254/2755-2721/2025.LD28791

Applied and Computational EngineeringOpen access

Construction of a Diabetes Prediction Model Based on Machine Learning

Research Article

Open Access

Construction of a Diabetes Prediction Model Based on Machine Learning

Ke Peng ^1*

¹ College of Information Engineering, China Ji Liang University, HangZhou, 310018, China

^*Corresponding author: 23h034160213@cjlu.edu.cn

Published on 28 October 2025

ACE Vol.202

ISSN (Print): 2755-273X

ISSN (Online): 2755-2721

ISBN (Print): 978-1-80590-497-7

ISBN (Online): 978-1-80590-498-4

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Abstract

This study investigates key predictors of diabetes risk across non-diabetes, prediabetes, and diabetes categories, while developing an optimal prediction model using multiple machine learning algorithms. Biomedical indicators such as HbA1c, urea, and creatinine, along with demographic factors like age and gender, were analyzed to evaluate their predictive value. Among the five algorithms tested, ensemble learning methods (CatBoost and XGBoost) outperformed traditional models, with CatBoost achieving the highest accuracy and demonstrating superior robustness. Feature importance analysis identified HbA1c as the most influential predictor, followed by age and BMI, aligning with established medical knowledge, whereas gender contributed minimally. The findings highlight the potential of advanced machine learning models, particularly CatBoost, in delivering highly accurate and stable diabetes risk prediction. This research provides strong technical support for early screening, targeted intervention, and practical risk assessment in diabetes management.

Keywords:

Diabetes Prediction, Machine Learning, CatBoost, HbA1c

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References

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References

[2]. International Diabetes Federation (IDF). (2025). IDF Diabetes Atlas. https: //diabetesatlas.org/

[3]. Office for National Statistics (ONS). (2024). Risk factors for pre-diabetes and undiagnosed type 2 diabetes in England: 2013 to 2019. https: //www.ons.gov.uk/

[4]. Marshal, P. (2025). Diabetes Prediction Dataset. Kaggle. https: //www.kaggle.com/datasets/marshalpatel3558/diabetes-prediction-dataset-legit-dataset

[6]. Zhang, Y., He, S., & You, S. (2019). Application of Ensemble Learning in Diabetes Prediction [J]. Intelligent Computer and Applications, 9(5): 176–179.

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[11]. World Health Organization. (2011). Use of glycated haemoglobin (HbA1c) in the diagnosis of diabetes mellitus: Abbreviated report of a WHO consultation.

Cite this article

Peng,K. (2025). Construction of a Diabetes Prediction Model Based on Machine Learning. Applied and Computational Engineering,202,39-46.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-MLA 2025 Symposium: Intelligent Systems and Automation: AI Models, IoT, and Robotic Algorithms

ISBN: 978-1-80590-497-7(Print) / 978-1-80590-498-4(Online)

Editor: Hisham AbouGrad

Conference website: https://www.confmla.org/london.html

Conference date: 12 November 2025

Series: Applied and Computational Engineering

Volume number: Vol.202

ISSN: 2755-2721(Print) / 2755-273X(Online)

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