Multivariate Models of Blood Glucose Prediction in Type1 Diabetes:
A Survey of the State-of-the-art

Sunny      Arora; Shailender      Kumar; Pardeep      Kumar
doi:10.2174/1389201023666220603092433
Abstract

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.
Keywords: Machine learning, Type1 diabetes, Regression, Glucose prediction, Hyperglycemia prediction, Diabetes mellitus
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Graphical Abstract

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DOI https://dx.doi.org/10.2174/1389201023666220603092433	Print ISSN 1389-2010
Publisher Name Bentham Science Publisher	Online ISSN 1873-4316
Current Pharmaceutical Biotechnology

Multivariate Models of Blood Glucose Prediction in Type1 Diabetes: A Survey of the State-of-the-art

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