№ 1 (1) 2025

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Open Access
Development of method to analyzefactors of kidney disease by the use of fuzzy logic
(SDU University, 2025) Assel Yembergenova; Azamat Serek; Bauyrzhan Berlikozha
The study introduces a new strategy for the analysis of kidney disease parameters based on fuzzy logic.Fuzzy logic is a more accurate way to categorize clinical parameters than statistical analysis because thereis uncertainty and variability in medical data. The data is comprised of an extensive amount of clinicalparameters including age, blood pressure, specific gravity, albumin, sugar, random blood glucose, bloodurea, serum creatinine, sodium, potassium, hemoglobin, packed cell volume, white blood cell count, andred blood cell count.The methodology utilizes fuzzy logic centroid computation to categorize these parameters into low,medium, and high levels to provide a more dynamic and interpretable assessment of renal health. Fuzzymemberships give the current work the capability to discover intricate interrelationships between clinicalvariables, which may have been otherwise unattainable by conventional mean, median, and standarddeviation-based analyses.The findings confirm that fuzzy logic and conventional statistical methods enhance the comprehensionof kidney disease by incorporating intricate interactions between clinical variables. The method is employedto achieve more accurate prediction and diagnostic models, offering insight to be used in kidney diseaseassessment and medical decisions.