{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T18:39:42Z","timestamp":1770143982274,"version":"3.49.0"},"reference-count":61,"publisher":"Wiley","license":[{"start":{"date-parts":[[2023,11,22]],"date-time":"2023-11-22T00:00:00Z","timestamp":1700611200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Applied Computational Intelligence and Soft Computing"],"published-print":{"date-parts":[[2023,11,22]]},"abstract":"<jats:p>Chronic kidney disease (CKD) is a progressive condition characterized by the gradual deterioration of kidney functions, potentially leading to kidney failure if not promptly diagnosed and treated. Machine learning (ML) algorithms have shown significant promise in disease diagnosis, but in healthcare, clinical data pose challenges: missing values, noisy inputs, and redundant features, affecting early-stage CKD prediction. Thus, this study presents a novel, fully automated machine learning approach to tackle these complexities by incorporating feature selection (FS) and feature space reduction (FSR) techniques, leading to a substantial enhancement of the model\u2019s performance. A data balancing technique is also employed during preprocessing to address data imbalance issue that is commonly encountered in clinical contexts. Finally, for reliable CKD classification, an ensemble characteristics-based classifier is encouraged. The effectiveness of our approach is rigorously validated and assessed on multiple datasets, and the clinical relevancy of the strategy is evaluated on the real-world therapeutic data collected from Bangladeshi patients. The study establishes the dominance of adaptive boosting, logistic regression, and passive aggressive ML classifiers with 96.48% accuracy in forecasting unseen therapeutic CKD data, particularly in early-stage cases. Furthermore, the effectiveness of the FSR technique in reducing the prediction time significantly is revealed. The outstanding performance of the proposed model demonstrates its effectiveness in addressing the complexity of healthcare CKD data by incorporating the FS and FSR techniques. This highlights its potential as a promising computer-aided diagnosis tool for doctors, enabling early interventions and improving patient outcomes.<\/jats:p>","DOI":"10.1155\/2023\/3140270","type":"journal-article","created":{"date-parts":[[2023,11,22]],"date-time":"2023-11-22T20:20:07Z","timestamp":1700684407000},"page":"1-17","source":"Crossref","is-referenced-by-count":12,"title":["An Intelligent Diagnostic System to Analyze Early-Stage Chronic Kidney Disease for Clinical Application"],"prefix":"10.1155","volume":"2023","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8594-0784","authenticated-orcid":true,"given":"N. I.","family":"Md. Ashafuddula","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Dhaka University of Engineering & Technology, Gazipur 1707, Bangladesh"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-4018-6780","authenticated-orcid":true,"given":"Bayezid","family":"Islam","sequence":"additional","affiliation":[{"name":"Department of Computer Science & Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0920-5697","authenticated-orcid":true,"given":"Rafiqul","family":"Islam","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Dhaka University of Engineering & Technology, Gazipur 1707, Bangladesh"}]}],"member":"311","reference":[{"key":"1","first-page":"260","article-title":"Chronic kidney disease prediction using machine learning methods","author":"I. U. Ekanayake"},{"key":"2","first-page":"887","article-title":"A novel machine learning approach chronic kidney disease prediction","author":"Y. D. S. Raju"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.4108\/eai.13-8-2021.170671"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.2307\/2136404"},{"key":"5","volume-title":"Chronic Kidney Disease","author":"J. M. Bargman","year":"2018"},{"key":"6","article-title":"Creating a bioartificial kidney as a permanent solution to kidney failure","author":"The Kidney Project","year":"2019"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.3329\/bjm.v33i3.61366"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1093\/ndt\/gfv466"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.1038\/ki.2015.230"},{"issue":"1","key":"10","first-page":"1","article-title":"Chronic kidney disease care in Indonesia: challenges and opportunities","volume":"55","author":"N. M. Hustrini","year":"2023","journal-title":"Acta Medica Indonesiana"},{"issue":"3","key":"11","first-page":"415","article-title":"Chronic kidney disease prevalence among health care providers in Bangladesh","volume":"19","author":"S. Das","year":"2010","journal-title":"Mymensingh Medical Journal: MMJ"},{"key":"12","doi-asserted-by":"publisher","DOI":"10.1186\/1744-8603-10-9"},{"key":"13","doi-asserted-by":"publisher","DOI":"10.1155\/2012\/267329"},{"key":"14","first-page":"222","article-title":"Computer aided diagnosis of thyroid disease using machine learning algorithms","author":"M. A.-A.-R. Asif"},{"key":"15","doi-asserted-by":"publisher","DOI":"10.1109\/access.2018.2798664"},{"key":"16","doi-asserted-by":"publisher","DOI":"10.1007\/s12325-021-01927-z"},{"key":"17","doi-asserted-by":"publisher","DOI":"10.1007\/s44174-022-00027-y"},{"key":"18","first-page":"432","article-title":"Classification and prediction analysis of diseases and other datasets using machine learning","author":"J. Nasir"},{"key":"19","doi-asserted-by":"publisher","DOI":"10.1109\/access.2021.3053763"},{"key":"20","doi-asserted-by":"publisher","DOI":"10.3390\/app11010202"},{"key":"21","doi-asserted-by":"publisher","DOI":"10.3390\/healthcare10020371"},{"key":"22","doi-asserted-by":"publisher","DOI":"10.2174\/1574893616666210616121023"},{"key":"23","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics12123138"},{"key":"24","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/2653665"},{"key":"25","doi-asserted-by":"publisher","DOI":"10.1109\/access.2021.3102399"},{"key":"26","first-page":"227","article-title":"L1-regulated feature selection and classification of microarray cancer data using deep learning","author":"B. Shekar"},{"key":"27","doi-asserted-by":"publisher","DOI":"10.1080\/02533839.2019.1676658"},{"key":"28","article-title":"A predictive analysis of chronic kidney disease by exploring important features","volume-title":"International Journal of Computing and Digital System","author":"M. Rahman","year":"2021"},{"key":"29","first-page":"429","article-title":"Classification system for prediction of chronic kidney disease using data mining techniques","author":"I. Saha"},{"key":"30","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpi.2023.100189"},{"key":"31","doi-asserted-by":"publisher","DOI":"10.32350\/bsr.0401.04"},{"key":"32","doi-asserted-by":"publisher","DOI":"10.1109\/access.2020.2981689"},{"key":"33","doi-asserted-by":"crossref","article-title":"Predictive analytics for chronic kidney disease using machine learning techniques","author":"A. Charleonnan","DOI":"10.1109\/MITICON.2016.8025242"},{"key":"34","doi-asserted-by":"publisher","DOI":"10.1109\/jtehm.2021.3073629"},{"key":"35","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1007\/978-981-13-5953-8_34","article-title":"Performance evaluation of ensemble-based machine learning techniques for prediction of chronic kidney disease","volume-title":"Emerging Research in Computing, Information, Communication and Applications","author":"K. Zubair Hasan","year":"2019"},{"key":"36","doi-asserted-by":"publisher","DOI":"10.14569\/ijacsa.2019.0100813"},{"key":"37","first-page":"1","article-title":"Optimization of prediction method of chronic kidney disease using machine learning algorithm","author":"P. Ghosh"},{"key":"38","first-page":"291","article-title":"Performance evaluation on machine learning classification techniques for disease classification and forecasting through data analytics for chronic kidney disease (ckd)","author":"W. Gunarathne"},{"key":"39","first-page":"300","article-title":"Dietary prediction for patients with chronic kidney disease (ckd) by considering blood potassium level using machine learning algorithms","author":"M. Wickramasinghe"},{"key":"40","article-title":"Categorical data examples and definitionaccessed","author":"S. Valcheva","year":"2022"},{"key":"41","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1168\/2\/022022"},{"key":"42","article-title":"Feature selection for classification: a review","volume":"37","author":"J. Tang","year":"2014","journal-title":"Data Classification: Algorithms and applications"},{"key":"43","doi-asserted-by":"publisher","DOI":"10.4236\/jilsa.2017.94006"},{"key":"44","article-title":"Enhancement in security by reducing dimensions of hyperspectral face images for face recognition","volume-title":"African Journal of Computing & ICTs","author":"S. Arya"},{"key":"45","doi-asserted-by":"publisher","DOI":"10.1109\/access.2020.2980942"},{"key":"46","article-title":"A review of dimension reduction techniques","author":"M. A. Carreira-Perpin\u00e1n","year":"1997"},{"key":"47","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2020.105400"},{"key":"48","article-title":"A municipal pm2. 5 forecasting method based on random forest and wrf model","volume-title":"Engineering Letters","author":"N. Jiang"},{"key":"49","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2019.105946"},{"key":"50","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.105606"},{"key":"51","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1013203451"},{"key":"52","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-020-03481-x"},{"key":"53","doi-asserted-by":"publisher","DOI":"10.1109\/ECACE.2019.8679388"},{"key":"54","first-page":"985","article-title":"Extreme learning machine: a new learning scheme of feedforward neural networks","author":"G.-B. Huang"},{"key":"55","volume-title":"UCI Machine Learning Repository","author":"L. J. Rubini","year":"2023"},{"key":"56","first-page":"952","article-title":"Risk factor prediction of chronic kidney disease based on machine learning algorithms","author":"M. A. Islam"},{"key":"57","first-page":"1","article-title":"Performance analysis of machine learning classifier for predicting chronic kidney disease","author":"R. Gupta"},{"key":"58","first-page":"2201","article-title":"Data cleaning: overview and emerging challenges","author":"X. Chu"},{"key":"59","doi-asserted-by":"publisher","DOI":"10.22452\/mjcs.sp2022no1.8"},{"key":"60","doi-asserted-by":"publisher","DOI":"10.3390\/bdcc6030098"},{"key":"61","doi-asserted-by":"publisher","DOI":"10.12720\/jait.14.2.384-391"}],"container-title":["Applied Computational Intelligence and Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/acisc\/2023\/3140270.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/acisc\/2023\/3140270.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/acisc\/2023\/3140270.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,22]],"date-time":"2023-11-22T20:20:14Z","timestamp":1700684414000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/acisc\/2023\/3140270\/"}},"subtitle":[],"editor":[{"given":"Nadeem","family":"Sarwar","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2023,11,22]]},"references-count":61,"alternative-id":["3140270","3140270"],"URL":"https:\/\/doi.org\/10.1155\/2023\/3140270","relation":{},"ISSN":["1687-9732","1687-9724"],"issn-type":[{"value":"1687-9732","type":"electronic"},{"value":"1687-9724","type":"print"}],"subject":[],"published":{"date-parts":[[2023,11,22]]}}}