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Review
. 2022 Dec 27;14(1):196.
doi: 10.1186/s13098-022-00969-9.

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

Elaheh Afsaneh  1 , Amin Sharifdini  2 , Hadi Ghazzaghi  2 , Mohadeseh Zarei Ghobadi  3
Affiliations
Review

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

Elaheh Afsaneh et al. Diabetol Metab Syndr. .

Abstract

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.

Keywords: Blood glucose; Deep learning; Gestational Diabetes Mellitus; Machine learning; Type 1 Diabetes Mellitus; Type 2 Diabetes Mellitus.

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Conflict of interest statement

The authors declare that they have no known competing financial or non-financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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