Fuzzy partitioning of clinical data for DMT2 patients

Miroslava Nedyalkova, Haruna L. Barazorda-Ccahuana, C. Sârbu, Sergio Madurga, Vasil Simeonov

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


The present study represents an original approach to data interpretation of clinical data for patients with diagnosis diabetes mellitus type 2 (DMT2) using fuzzy clustering as a tool for intelligent data analysis. Fuzzy clustering is often used in classification and interpretation of medical data (including in medical diagnosis studies) but in this study it is applied with a different goal: to separate a group of 100 patients with DMT2 from a control group of healthy volunteers and, further, to reveal three different patterns of similarity between the patients. Each pattern is described by specific descriptors (variables), which ensure pattern interpretation by appearance of underling disease to DMT2.

Original languageEnglish
Pages (from-to)1450-1458
Number of pages9
JournalJournal of Environmental Science and Health - Part A Toxic/Hazardous Substances and Environmental Engineering
Issue number12
StatePublished - 2020
Externally publishedYes


  • Fuzzy clustering
  • diabetes mellitus type 2
  • exploratory data
  • underlying diseases


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