Аннотации:
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.