{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T07:02:00Z","timestamp":1777705320399,"version":"3.51.4"},"reference-count":21,"publisher":"SAGE Publications","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,1,30]]},"abstract":"<jats:p>Early identification of chronic kidney disease (CKD) becomes essential to reduce the severity level and mortality rate. Since medical diagnoses are equipped with latest technologies such as machine learning (ML), data mining, and artificial intelligence, they can be employed to diagnose the disease and aid decision making process. Since the accuracy of the classification model greatly depends upon the number of features involved, the feature selection (FS) approaches are developed which results in improved accuracy. With this motivation, this study designs a novel chaotic binary black hole based feature selection with classification model for CKD diagnosis, named CBHFSC-CKD technique. The proposed CBHFSC-CKD technique encompasses the design of chaotic black hole based feature selection (CBH-FS) to choose an optimal subset of features and thereby enhances the diagnostic performance. In addition, the bacterial colony algorithm (BCA) with kernel extreme learning machine (KELM) classifier is applied for the identification of CKD. Moreover, the design of BCA to optimally adjust the parameters involved in the KELM results in improved classification performance. A comprehensive set of simulation analyses is carried out and the results are inspected interms of different aspects. The simulation outcome pointed out the supremacy of the CBHFSC-CKD technique compared to other recent techniques interms of different measures.<\/jats:p>","DOI":"10.3233\/jifs-220994","type":"journal-article","created":{"date-parts":[[2022,11,22]],"date-time":"2022-11-22T11:01:20Z","timestamp":1669114880000},"page":"2999-3010","source":"Crossref","is-referenced-by-count":1,"title":["Design of chaotic black hole based feature selection with classification for Chronic Kidney Disease diagnosis"],"prefix":"10.1177","volume":"44","author":[{"given":"M.","family":"Gokiladevi","sequence":"first","affiliation":[{"name":"Department of Computer Science, Alagappa University, Karaikudi, Tamil Nadu, India"}]},{"given":"Sundar","family":"Santhoshkumar","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Alagappa University, Karaikudi, Tamil Nadu, India"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-220994_ref3","unstructured":"Nishat M.M. , Dip R.R. , Faisal F. , Nasrullah S.M. , Ahsan R. , Shikder M.F. , Asif M.A.A.R. and Hoque M.A. , A Comprehensive Analysis on Detecting Chronic Kidney Disease by Employing Machine Learning Algorithms, EAI Endorsed Transactions on Pervasive Health and Technology 18(e6) (2021)."},{"key":"10.3233\/JIFS-220994_ref4","doi-asserted-by":"crossref","first-page":"4438","DOI":"10.1016\/j.eswa.2013.01.046","article-title":"An end stage kidney disease predictor based on an artificial neural networks ensemble","volume":"40","author":"Di Noia","year":"2013","journal-title":"Expert Syst Appl"},{"issue":"1","key":"10.3233\/JIFS-220994_ref5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-019-46074-2","article-title":"Intelligent diagnostic prediction and classification system for chronic kidney disease","volume":"9","author":"Elhoseny","year":"2019","journal-title":"Scientific 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