Time Series Prediction Models for Diabetes: A Systematic Literature Review

Wissem Mbarek, Nesrine Khabou, Lotfi Souifi, Ismael Rodriguez

2025

Abstract

Diabetes is a highly prevalent chronic disease that imposes significant health and economic burdens globally. Early and accurate prediction, along with timely intervention, is crucial to prevent or delay the onset of diabetes and its complications. Various techniques have been used to forecast this disease, one of them is time series analysis, which has shown promise in the field of diabetes research prediction. This comprehensive review examines the existing literature on time series prediction models for diabetes, identifying the various machine learning and statistical methods employed, including recurrent neural networks, long short-term memory networks, integrated auto-regressive moving average models and hybrid approaches. The review highlights key time series parameters, such as glucose levels, insulin dosage, diet, physical activity, and other physiological metrics, that significantly impact predictive precision and overall performance of these models. The findings of this review provide valuable insight into the current state of time series prediction models for diabetes, underscoring the strengths and limitations of each approach.

Paper Citation

in Harvard Style

Mbarek W., Khabou N., Souifi L. and Rodriguez I. (2025). Time Series Prediction Models for Diabetes: A Systematic Literature Review. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 1352-1359. DOI: 10.5220/0013368400003890

in Bibtex Style

@conference{icaart25,
author={Wissem Mbarek and Nesrine Khabou and Lotfi Souifi and Ismael Rodriguez},
title={Time Series Prediction Models for Diabetes: A Systematic Literature Review},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2025},
pages={1352-1359},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013368400003890},
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 - Time Series Prediction Models for Diabetes: A Systematic Literature Review
SN - 978-989-758-737-5
AU - Mbarek W.
AU - Khabou N.
AU - Souifi L.
AU - Rodriguez I.
PY - 2025
SP - 1352
EP - 1359
DO - 10.5220/0013368400003890
PB - SciTePress