Comparative development and evaluation of multilayer fuzzy and adaptive neuro-fuzzy inference systems for early diagnosis of chronic kidney disease
Year: 2025
Type: article
Abstract: Chronic Kidney Disease (CKD) is a gradual yet life-threatening condition that often remains undetected until significant renal impairment has occurred. Early recognition is essential to slow its progression and reduce the risk of associated complications. Conventional diagnostic procedures have limitations; they are subjective and may be ineffective when symptoms take longer to manifest. In this paper, we propose and compare two intelligent diagnostic models—a Multilayer Fuzzy Inference System (MLFIS) and an Adaptive Neuro-Fuzzy Inference System (ANFIS)—that aim to diagnose early-stage CKD using clinical symptoms and laboratory indicators. The model of MLFIS is constructed in dual form i.e. the first layer determines the probable presence of CKD, which is based on symptoms like hypertension, diabetes, and family history, and the second layer specifies the disease stage, which is done based on biochemical parameters attached with or without or increasing glomerular filtration rate, blood urea nitrogen, serum creatinine, albumin, and urinary pus cells. The ANFIS model, however, combines the strengths of both fuzzy logic and neural networks, providing automatic learning of diagnosis rules based on data, thereby increasing accuracy and flexibility. The models had been constructed and benchmarked on a retrospective dataset consisting of 500 anonymised patient records retrieved from a publicly available resource, and then tested using standard performance measures. Although the ANFIS had a classification accuracy of 99.5 % which is a higher percentage than the 99.33 per cent accuracy of the MLFIS, the difference is not that great. Findings demonstrate the benchmarking performance of fuzzy and neuro-fuzzy systems for CKD staging, providing a foundation for future validation on hospital datasets. However, it is important to note that the high accuracies reported are based on a relatively small UCI dataset and should be interpreted as internal validation only. Broader clinical validation is necessary before deployment in real-world settings. The paper also compares these two systems and determines their performance based on current machine learning models. This study seeks to fill the gap between the laboratory-based diagnostic and real clinical practice by presenting a scalable and understandable solution to screening of CKD.
Source: Informatics in Medicine Unlocked
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,470
OpenAlex ID: W4417299206