Classification of adaptive back propagation neural network along with fuzzy logic in chronic kidney disease
subject: (ABPNN-ANFIS); Chronic kidney disease (CKD); DL algorithms; MATLAB; UCI CKD Dataset
description: A steady deterioration in kidney function over months or years is known as chronic kidney disease (CKD). Through a range of techniques, such as pharmacological intervention in moderate cases and hemodialysis and renal transport in severe cases, early identification of CKD is crucial and has a substantial influence on reducing the patient's health development. The outcomes show the patient's kidneys' present state. It is suggested to develop a system for detecting chronic renal disease using machine learning. Finding the best feature sets typically involves using metaheuristic algorithms since feature selection is an NP-hard issue with amorphous polynomials. Semi-crystalline tabu search (TS) is frequently used for both local and global searches. In this study, we employ a brand-new hybrid TS with stochastic diffusion search (SDX)-based feature selection. The adaptive backpropagation neural network (ABPNN-ANFIS) is then classified using fuzzy logic. Fuzzy logic may be used to combine the ABPNN findings. Consequently, these techniques can aid experts in determining the stage of chronic renal disease. The Adaptive Neuron Clearing Inference System (ABPNN-ANFIS) was utilised to develop adaptive inverse neural networks using the MATLAB programme. The outcomes demonstrate that the suggested ABPNN-ANFIS is 98 % accurate in terms of efficiency. 2024
creator: KR V.; Maharajan M.S.; K B.; Sivakumar N.
source: e-Prime - Advances in Electrical Engineering, Electronics and Energy, Vol-7
publisher: Elsevier Ltd
date: 2024-01-01
rights: All Open Access; Gold Open Access
relation: ISSN: 27726711
format: Online
language: English
type: Article
coverage: KR V., Computer Science, Christ Deemed to be University, Karnataka, Bangalore, 560029, India; Maharajan M.S., Department of ECE, SRM Institute of Science and Technology, Chennai, Kattankulathur, India; K B., Electronics and Communication Engineering, Alva's Institute of Engineering and Technology, Mijar, Moodbidri, Karnataka, 74225, India; Sivakumar N., Artificial Intelligence & Data Science, Panimalar Engineering College, Tamilnadu, Chennai, 600123, India