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September 23, 2025International Journal of Science and Research (IJSR)Open Access

Unsupervised Cluster Analysis of Diabetes Mellitus: A Systemic Review from Eastern Indian Population

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Authors

DSDevendra Prasad SinghMKMomin Khan

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Overview

Systematic review evaluates unsupervised cluster analysis for diabetes phenotyping, suggesting distinct complication risks.

Key Points

  • Unsupervised cluster analysis effectively identifies distinct diabetes subgroups, enhancing patient management and treatment outcomes.
  • Five clusters were reproducibly identified, with MOD and MARD being most frequent; SIDD was notably higher among Asian individuals.
  • The cross-sectional study used K-means cluster analysis on clinical data from 625 diabetes patients, indicating significant cluster variances.
  • These findings support the use of advanced clustering techniques, highlighting potential undetected diabetes subtypes and their characteristics.

Cite This Study

Singh et al. (2025) studied this question.

synapsesocial.com/papers/68d43d68713b0b5dfea7cf4ehttps://doi.org/10.21275/sr25918163728
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