Big data and machine learning to tackle diabetes management.

ABSTRACT:

Background

Type 2 Diabetes (T2D) diagnosis is based solely on glycaemia, even though it is an endpoint of numerous dysmetabolic pathways. Type 2 Diabetes complexity is challenging in a real-world scenario; thus, dissecting T2D heterogeneity is a priority. Cluster analysis, which identifies natural clusters within multidimensional data based on similarity measures, poses a promising tool to unravel Diabetes complexity.

Methods

In this review, we scrutinize and integrate the results obtained in most of the works up to date on cluster analysis and T2D.

Results

To correctly stratify subjects and to differentiate and individualize a preventive or therapeutic approach to Diabetes management, cluster analysis should be informed with more parameters than the traditional ones,

SUBMITTER: Pina AF 

PROVIDER: S-EPMC10078354 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature