PREDICTION OF CHRONIC KIDNEY DISEASE (CKD) USING A FUZZY INFERENCE SYSTEM    

Authors : 1. HEMLATA SAHAY; 2. SHUBHNESH KUMAR GOYA

Publishing Date : 2023

DOI : https://doi.org/10.52458/9789388996570.2023.eb.ch43

ISBN : 978-93-88996-58-7

Pages : 222

Chapter id : NSP/ICAAR-2023/A-43

Abstract : Early diagnosis of CKD is vital in the fight to limit the number of people affected by the disease. As such, the goal of this research is to use the predictive model for CKD detection in MATLAB (fuzzy logic toolbox). The process of creating the model consists of five stages. At first, we established what tests—blood urea nitrogen, eGFR (estimated glomerular filtration rate), and serum creatinine—were used as input data. Second, min-max normalization processing was used to fuzzification of the inputs and outputs. After that, we built an inference engine and aggregated our rules. Finally, defuzzification of the predictive model outputs was used to examine each patient's CKD status. In conclusion, detecting CKD early is crucial for treating the condition effectively. A nephrologists or other specialist can then be consulted for further care before any major concerns arise.

Keywords : fuzzy inference system, MATLAB, CKD, eGFR, blood urea nitrogen test

Cite : Sahay, H., & Goya, S. K. (2023). Prediction of Chronic Kidney Disease (Ckd) Using A Fuzzy Inference System (1st ed., pp. 222-231). Noble Science Press. https://doi.org/10.52458/9789388996570.2023.eb.ch43

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