{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T15:17:20Z","timestamp":1784215040509,"version":"3.55.0"},"reference-count":21,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,8,29]],"date-time":"2022-08-29T00:00:00Z","timestamp":1661731200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Sciences and Engineering Research Council of Canada (NSERC)"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Chronic kidney disease (CKD) is one of the most life-threatening disorders. To improve survivability, early discovery and good management are encouraged. In this paper, CKD was diagnosed using multiple optimized neural networks against traditional neural networks on the UCI machine learning dataset, to identify the most efficient model for the task. The study works on the binary classification of CKD from 24 attributes. For classification, optimized CNN (OCNN), ANN (OANN), and LSTM (OLSTM) models were used as well as traditional CNN, ANN, and LSTM models. With various performance matrixes, error measures, loss values, AUC values, and compilation time, the implemented models are compared to identify the most competent model for the classification of CKD. It is observed that, overall, the optimized models have better performance compared to the traditional models. The highest validation accuracy among the tradition models were achieved from CNN with 92.71%, whereas OCNN, OANN, and OLSTM have higher accuracies of 98.75%, 96.25%, and 98.5%, respectively. Additionally, OCNN has the highest AUC score of 0.99 and the lowest compilation time for classification with 0.00447 s, making it the most efficient model for the diagnosis of CKD.<\/jats:p>","DOI":"10.3390\/a15090308","type":"journal-article","created":{"date-parts":[[2022,8,29]],"date-time":"2022-08-29T01:39:19Z","timestamp":1661737159000},"page":"308","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["Early Prediction of Chronic Kidney Disease: A Comprehensive Performance Analysis of Deep Learning Models"],"prefix":"10.3390","volume":"15","author":[{"given":"Chaity","family":"Mondol","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Daffodil International University, Dhaka 1207, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9176-3537","authenticated-orcid":false,"given":"F. M. Javed Mehedi","family":"Shamrat","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, Daffodil International University, Dhaka 1207, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Md. Robiul","family":"Hasan","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Daffodil International University, Dhaka 1207, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saidul","family":"Alam","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Daffodil International University, Dhaka 1207, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5524-2328","authenticated-orcid":false,"given":"Pronab","family":"Ghosh","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Lakehead University, 955 Oliver Road, Thunder Bay, ON P7B 5E1, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zarrin","family":"Tasnim","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, Daffodil International University, Dhaka 1207, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4034-9819","authenticated-orcid":false,"given":"Kawsar","family":"Ahmed","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK S7N 5A9, Canada"},{"name":"Group of Bio-Photomati\u03c7 Department of Information and Communication Technology, Mawlana Bhashani Science and Technology University, Tangail 1902, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francis M.","family":"Bui","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK S7N 5A9, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sobhy M.","family":"Ibrahim","sequence":"additional","affiliation":[{"name":"Department of Biochemistry, College of Science, King Saud University, Riyadh 11451, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"35425","DOI":"10.1007\/s11042-019-07839-z","article-title":"KDSAE: Chronic kidney disease classification with multimedia data learning using deep stacked autoencoder network","volume":"79","author":"Khamparia","year":"2020","journal-title":"Multimed. 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