Recent trends and techniques of blood glucose level prediction for diabetes control

Diabetes, a metabolic disorder disease, can cause short-term acute or even long-term chronic complications in a patient's body. In 2021, 10.5% of the world's adult population had diabetes. These numbers are increasing day by day, which results in an associated increase of morbidity, mortality, and health care cost related to diabetes. Thus, a huge research effort has been carried out to manage diabetes. A precursor to diabetes management is to predict the future blood glucose levels based on a patient's past history. In this paper, we provide a comprehensive and systematic study of diabetes management, focusing on recent research towards blood glucose level prediction. In particular, we have categorized and presented existing recent research based on major clinical application domains, different input features, and major modeling techniques including physiological, data-driven, and hybrid models. We have summarized the performance analysis of different modeling techniques using different metrics, and critically analyzed these techniques from different perspectives. Finally, we have identified a number of research challenges and potential future works that range from data collection to model improvement for Type 2 Diabetes Mellitus. This review can be a good starting point for researchers and practitioners who are working in building data-driven computational models for diabetes management and blood glucose level prediction.