Key result
Machine learning models, including time series and neural networks, are evaluated for predicting blood glucose dynamics and hyper/hypoglycemia in patients with Type 1 diabetes.
Why the study?
Diabetes mellitus is a chronic disorder with high global prevalence, and this survey was conducted to assess classical time series, neural networks, and state-of-the-art regression models to predict blood glucose and hyper/hypoglycemia in Type 1 diabetic patients.
This survey highlights the potential of machine learning models in predicting blood glucose dynamics to aid in the management of Type 1 diabetes.
ML models for T1D glucose prediction merit prospective validation; leaves open optimal clinical integration.
Diabetes mellitus is a long-term chronicle disorder with a high prevalence rate worldwide. Continuous blood glucose and lifestyle monitoring enabled the control of blood glucose dynamics through machine learning applications using data created by various popular sensors. This survey aims to assess various classical time series, neural networks and state-of-the-art regression models based on a wide variety of machine learning techniques to predict blood glucose and hyper/hypoglycemia in Type 1 diabetic patients. The analysis covers blood glucose prediction modeling, regression, hyper/hypoglycemia alerts, diabetes diagnosis, monitoring, and management. However, the primary focus is on evaluating models for the prediction of Type 1 diabetes. A wide variety of machine learning algorithms have been explored to implement precision medicine by clinicians and provide patients with an early warning system. The automated pancreas may benefit from predictions and alerts of hyper and hypoglycemia.
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Kumar et al. (2022) conducted a review in Type 1 Diabetes. Machine learning models was evaluated. Machine learning models, including time series and neural networks, are evaluated for predicting blood glucose dynamics and hyper/hypoglycemia in patients with Type 1 diabetes.
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