Machine Learning-Driven Monitoring for Early Detection and Management of Prediabetes

Wesam A. Ali, Adeem Ali Anwar

2025

Abstract

Prediabetes is a critical metabolic condition that acts as the precursor for type 2 diabetes (T2D). Early detection and management of prediabetes can prevent the onset of diabetes and associated complications. For individuals with prediabetes, having a reliable way to estimate their risk of developing T2D is crucial, as it helps them to keep their glycemic levels on track and may even enable them to regress to normoglycemia. Building on this, we propose a methodology to predict the progression rate of prediabetes. In this study, we enhanced the preexisting dataset by incorporating risk progression and risk probability using logistic regression. Moreover, we predicted the progression rate of prediabetes using machine learning-based approaches and performed comparative analysis using logistic regression, random forest, decision tree, gradient boosting, neural networks, and support vector machines. Utilizing key health indicators such as age, body mass index (BMI), gender, and comorbidities as characteristic factors of prediabetes progression. The results demonstrate that logistic regression outperforms other models with an accuracy of 99.93%, a precision of 99.92%, and an AUC-ROC of 1.0000, making it the most suitable model for predicting prediabetes risk. The proposed system offers a promising solution for real-time prediabetes monitoring.

Paper Citation

in Harvard Style

Ali W. and Anwar A. (2025). Machine Learning-Driven Monitoring for Early Detection and Management of Prediabetes. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 256-263. DOI: 10.5220/0013118800003890

in Bibtex Style

@conference{icaart25,
author={Wesam Ali and Adeem Anwar},
title={Machine Learning-Driven Monitoring for Early Detection and Management of Prediabetes},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2025},
pages={256-263},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013118800003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Machine Learning-Driven Monitoring for Early Detection and Management of Prediabetes
SN - 978-989-758-737-5
AU - Ali W.
AU - Anwar A.
PY - 2025
SP - 256
EP - 263
DO - 10.5220/0013118800003890
PB - SciTePress