The Identification of Diabetes Mellitus Subtypes Applying Cluster Analysis Techniques: A Systematic Review

Antonio Sarría-Santamera, Binur Orazumbekova, Tilektes Maulenkul, Abduzhappar Gaipov and Kuralay Atageldiyeva
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Antonio Sarría-Santamera: Department of Medicine, Nazarbayev University School of Medicine, Nur-Sultan 010000, Kazakhstan
Binur Orazumbekova: Department of Medicine, Nazarbayev University School of Medicine, Nur-Sultan 010000, Kazakhstan
Tilektes Maulenkul: Department of Medicine, Nazarbayev University School of Medicine, Nur-Sultan 010000, Kazakhstan
Abduzhappar Gaipov: Department of Medicine, Nazarbayev University School of Medicine, Nur-Sultan 010000, Kazakhstan
Kuralay Atageldiyeva: Department of Medicine, Nazarbayev University School of Medicine, Nur-Sultan 010000, Kazakhstan

IJERPH, 2020, vol. 17, issue 24, 1-18

Abstract: Diabetes Mellitus is a chronic and lifelong disease that incurs a huge burden to healthcare systems. Its prevalence is on the rise worldwide. Diabetes is more complex than the classification of Type 1 and 2 may suggest. The purpose of this systematic review was to identify the research studies that tried to find new sub-groups of diabetes patients by using unsupervised learning methods. The search was conducted on Pubmed and Medline databases by two independent researchers. All time publications on cluster analysis of diabetes patients were selected and analysed. Among fourteen studies that were included in the final review, five studies found five identical clusters: Severe Autoimmune Diabetes; Severe Insulin-Deficient Diabetes; Severe Insulin-Resistant Diabetes; Mild Obesity-Related Diabetes; and Mild Age-Related Diabetes. In addition, two studies found the same clusters, except Severe Autoimmune Diabetes cluster. Results of other studies differed from one to another and were less consistent. Cluster analysis enabled finding non-classic heterogeneity in diabetes, but there is still a necessity to explore and validate the capabilities of cluster analysis in more diverse and wider populations.

Keywords: diabetes; novel sub-groups; unsupervised learning techniques; cluster analysis (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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