Authors:
Wissem Mbarek
;
Nesrine Khabou
;
Lotfi Souifi
and
Ismael Rodriguez
Affiliation:
ReDCAD Laboratory, ENIS, University of Sfax, Tunisia
Keyword(s):
AI, Time Series, Prediction Models, Diabetes, Systematic Literature Review, SLR.
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 re
view provide valuable insight into the current state of time series prediction models for diabetes, underscoring the strengths and limitations of each approach.
(More)