Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Classification of adaptive back propagation neural network along with fuzzy logic in chronic kidney disease Subject The topic of the resource (ABPNN-ANFIS); Chronic kidney disease (CKD); DL algorithms; MATLAB; UCI CKD Dataset Description An account of the resource 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 An entity primarily responsible for making the resource KR V.; Maharajan M.S.; K B.; Sivakumar N. Source A related resource from which the described resource is derived e-Prime - Advances in Electrical Engineering, Electronics and Energy, Vol-7 Publisher An entity responsible for making the resource available Elsevier Ltd Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.prime.2024.100463" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.prime.2024.100463</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186496923&amp;doi=10.1016%2Fj.prime.2024.100463&amp;partnerID=40&amp;md5=a0bb55f1bc8a8412e96b3d50ccd79b42" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186496923&amp;doi=10.1016%2fj.prime.2024.100463&amp;partnerID=40&amp;md5=a0bb55f1bc8a8412e96b3d50ccd79b42</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 27726711 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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 &amp; Data Science, Panimalar Engineering College, Tamilnadu, Chennai, 600123, India