Fuzzy partitioning of clinical data for DMT2 patients. Issue 12 (14th October 2020)

• Record Type: Journal Article
• Authors: Nedyalkova, Miroslava; Barazorda-Ccahuana, Haruna L.; Sârbu, C.; Madurga, Sergio; Simeonov, Vasil
• Is Part Of: Journal of environmental science and health. Volume 55:Issue 12(2020)
• Page Start: 1450
• Page End: 1458
• Publication Date: 2020-10-14
• Subjects: Fuzzy clustering -- diabetes mellitus type 2 -- underlying diseases -- exploratory data; Environmental engineering -- Periodicals; Environmental sciences -- Periodicals; Ecology -- periodicals; Hazardous Substances -- periodicals; 628
• DOI: 10.1080/10934529.2020.1809925
• Languages: English
• ISSNs: 1093-4529

Abstract

Abstract: 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.