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

被引:35
作者
Afsaneh, Elaheh
Sharifdini, Amin [1 ]
Ghazzaghi, Hadi [1 ]
Ghobadi, Mohadeseh Zarei
机构
[1] Sedna Teb Mobin, Tehran, Iran
关键词
Type 1 Diabetes Mellitus; Type 2 Diabetes Mellitus; Gestational Diabetes Mellitus; Machine learning; Deep learning; Blood glucose; CARDIOVASCULAR RISK-FACTORS; BLOOD-GLUCOSE CONCENTRATION; INSULIN-RESISTANCE; PHYSICAL-ACTIVITY; GLYCEMIC CONTROL; NEURAL-NETWORKS; REGRESSION; HYPOGLYCEMIA; CLASSIFICATION; PREGNANCY;
D O I
10.1186/s13098-022-00969-9
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
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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页数:39
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