Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review

Diabetes as a metabolic illness can be characterized by increased amounts of blood glucose. This abnormal increase can lead to critical detriment to the other organs such as the kidneys, eyes, heart, nerves, and blood vessels. Therefore, its prediction, prognosis, and management are essential to pre...

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Published in:Diabetology and metabolic syndrome Vol. 14; no. 1; pp. 196 - 39
Main Authors: Afsaneh, Elaheh, Sharifdini, Amin, Ghazzaghi, Hadi, Ghobadi, Mohadeseh Zarei
Format: Journal Article
Language:English
Published: London BioMed Central 27.12.2022
BioMed Central Ltd
BMC
Subjects:
Algorithms
Blood glucose
Blood sugar
Care and treatment
Data mining
Deep learning
Diabetes
Diabetes therapy
Endocrinology
Gestational Diabetes Mellitus
Machine learning
Medicine
Medicine & Public Health
Metabolic Diseases
Prognosis
Review
Surveys
Type 1 diabetes
Type 1 Diabetes Mellitus
Type 2 diabetes
Type 2 Diabetes Mellitus
Deep learning
Blood glucose
Gestational Diabetes Mellitus
Type 1 Diabetes Mellitus
Type 2 Diabetes Mellitus
Machine learning
ISSN:1758-5996, 1758-5996
Online Access:Get full text
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Summary:Diabetes as a metabolic illness can be characterized by increased amounts of blood glucose. This abnormal increase can lead to critical detriment to the other organs such as the kidneys, eyes, heart, nerves, and blood vessels. Therefore, its prediction, prognosis, and management are essential to prevent harmful effects and also recommend more useful treatments. For these goals, machine learning algorithms have found considerable attention and have been developed successfully. This review surveys the recently proposed machine learning (ML) and deep learning (DL) models for the objectives mentioned earlier. The reported results disclose that the ML and DL algorithms are promising approaches for controlling blood glucose and diabetes. However, they should be improved and employed in large datasets to affirm their applicability.
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ISSN:1758-5996
1758-5996
DOI:10.1186/s13098-022-00969-9