{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T23:21:33Z","timestamp":1782948093408,"version":"3.54.5"},"reference-count":43,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF2021R1I1A2059735"],"award-info":[{"award-number":["NRF2021R1I1A2059735"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["DIGITAL HEALTH"],"published-print":{"date-parts":[[2023,1]]},"abstract":"<jats:p>Early identification of children with self-care impairments is one of the key challenges professional therapists face due to the complex and time-consuming detection process using relevant self-care activities. Due to the complex nature of the problem, machine-learning methods have been widely applied in this area. In this study, a feed-forward artificial neural network (ANN)-based self-care prediction methodology, called multilayer perceptron (MLP)-progressive, has been proposed. The proposed methodology integrates unsupervised instance-based resampling and randomizing preprocessing techniques to MLP for improved early detection of self-care disabilities in children. Preprocessing of the dataset affects the MLP performance; hence, randomization and resampling of the dataset improves the performance of the MLP model. To confirm the usefulness of MLP-progressive, three experiments were conducted, including validating MLP-progressive methodology over multi-class and binary-class datasets, impact analysis of the proposed preprocessing filters on the model performance, and comparing the MLP-progressive results with state-of-the-art studies. The evaluation metrics accuracy, precision, recall, F-measure, TP rate, FP rate, and ROC were used to measure performance of the proposed disability detection model. The proposed MLP-progressive model outperforms existing methods and attains a classification accuracy of 97.14% and 98.57% on multi-class and binary-class datasets, respectively. Additionally, when evaluated on the multi-class dataset, significant improvements in accuracies ranging from 90.00% to 97.14% were observed when compared to state-of-the-art methods.<\/jats:p>","DOI":"10.1177\/20552076231184054","type":"journal-article","created":{"date-parts":[[2023,6,30]],"date-time":"2023-06-30T01:21:35Z","timestamp":1688088095000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":8,"title":["Multilayer perceptron-based self-care early prediction of children with disabilities"],"prefix":"10.1177","volume":"9","author":[{"given":"Rahman","family":"Ali","sequence":"first","affiliation":[{"name":"Quaid-e-Azam College of Commerce, University of Peshawar, Khyber Pakhtunkhwa, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jamil","family":"Hussain","sequence":"additional","affiliation":[{"name":"Department of Data Science, Sejong University, Seoul, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5632-5208","authenticated-orcid":false,"given":"Seung Won","family":"Lee","sequence":"additional","affiliation":[{"name":"Sungkyunkwan University School of Medicine, Suwon, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2023,6,29]]},"reference":[{"key":"bibr1-20552076231184054","unstructured":"Wu T-K, Huang S-C, Meng Y-R.\n                      Identifying and diagnosing students with learning disabilities using ANN and SVM\n                      . in\n                      The 2006 IEEE International Joint Conference on Neural Network Proceedings\n                      . 2006. IEEE."},{"key":"bibr2-20552076231184054","first-page":"13","volume":"11","author":"Haley SM","year":"1992","journal-title":"Phys Occup Ther Pediatr"},{"key":"bibr3-20552076231184054","volume-title":"International Classification of Functioning, Disability, and Health: Children & Youth Version: ICF-CY","author":"Organization WH","year":"2007"},{"key":"bibr4-20552076231184054","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2018.03.003"},{"key":"bibr5-20552076231184054","doi-asserted-by":"publisher","DOI":"10.1109\/ICCITECHN.2018.8631960"},{"key":"bibr6-20552076231184054","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2019.8852324"},{"key":"bibr7-20552076231184054","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.112875"},{"key":"bibr8-20552076231184054","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-020-04943-2"},{"key":"bibr9-20552076231184054","doi-asserted-by":"publisher","DOI":"10.1109\/FUZZ-IEEE.2019.8858906"},{"key":"bibr10-20552076231184054","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/2794888"},{"key":"bibr11-20552076231184054","doi-asserted-by":"publisher","DOI":"10.1089\/eco.2017.0011"},{"key":"bibr12-20552076231184054","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-020-00316-7"},{"key":"bibr13-20552076231184054","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bti499"},{"key":"bibr14-20552076231184054","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/10095020.2021.1978277","volume":"25","author":"Rodrigues BP","year":"2022","journal-title":"Geo-spatial Inf Sci"},{"key":"bibr15-20552076231184054","doi-asserted-by":"publisher","DOI":"10.1007\/BF01868317"},{"key":"bibr16-20552076231184054","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2022.730748"},{"key":"bibr17-20552076231184054","doi-asserted-by":"publisher","DOI":"10.1109\/ICECA.2018.8474918"},{"key":"bibr18-20552076231184054","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4842-3799-1"},{"key":"bibr19-20552076231184054","doi-asserted-by":"publisher","DOI":"10.3390\/sym11010089"},{"key":"bibr20-20552076231184054","doi-asserted-by":"publisher","DOI":"10.3390\/math8091590"},{"key":"bibr21-20552076231184054","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2021.753033"},{"key":"bibr22-20552076231184054","doi-asserted-by":"publisher","DOI":"10.3233\/THC-219008"},{"key":"bibr23-20552076231184054","first-page":"1879","volume":"12","author":"Sharmila M","year":"2021","journal-title":"Turkish J Comput Math Educ (TURCOMAT)"},{"key":"bibr24-20552076231184054","first-page":"1","volume":"14","author":"Sharma M","year":"2020","journal-title":"Int J Inf Technol"},{"key":"bibr25-20552076231184054","doi-asserted-by":"publisher","DOI":"10.1109\/CSDE50874.2020.9411531"},{"key":"bibr26-20552076231184054","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLA.2019.00245"},{"key":"bibr27-20552076231184054","unstructured":"Lin J, et al.\n                      Measuring the Effect of Training Data on Deep Learning Predictions via Randomized Experiments\n                      . in\n                      International Conference on Machine Learning\n                      . 2022. PMLR."},{"key":"bibr28-20552076231184054","doi-asserted-by":"publisher","DOI":"10.1145\/3560828.3564001"},{"key":"bibr29-20552076231184054","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2019.103319"},{"key":"bibr30-20552076231184054","first-page":"100267","volume":"8","author":"Thejas G","year":"2022","journal-title":"Mach Learn Appl"},{"key":"bibr31-20552076231184054","doi-asserted-by":"publisher","DOI":"10.1145\/1531326.1531330"},{"key":"bibr32-20552076231184054","doi-asserted-by":"publisher","DOI":"10.1109\/IS.2016.7737456"},{"key":"bibr33-20552076231184054","unstructured":"Gallicchio C, et al.\n                      Randomized Machine Learning Approaches: Recent Developments and Challenges\n                      . in\n                      ESANN\n                      . 2017."},{"key":"bibr34-20552076231184054","unstructured":"Bento C.\n                      Multilayer Perceptron explained with a real-life example and python code: sentiment analysis.\n                      Towards Data Science, 2021."},{"key":"bibr35-20552076231184054","doi-asserted-by":"publisher","DOI":"10.1017\/S0962492900002919"},{"key":"bibr36-20552076231184054","doi-asserted-by":"publisher","DOI":"10.1016\/S0167-9473(02)00257-8"},{"key":"bibr37-20552076231184054","doi-asserted-by":"publisher","DOI":"10.1016\/j.spl.2021.109100"},{"key":"bibr38-20552076231184054","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1983.10478008"},{"key":"bibr39-20552076231184054","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2011.5946475"},{"key":"bibr40-20552076231184054","doi-asserted-by":"publisher","DOI":"10.1007\/s11634-022-00491-w"},{"key":"bibr41-20552076231184054","unstructured":"Zelaya CVG.\n                      Towards explaining the effects of data preprocessing on machine learning\n                      . in 2019\n                      IEEE 35th international conference on data engineering (ICDE)\n                      . 2019. IEEE."},{"key":"bibr42-20552076231184054","doi-asserted-by":"publisher","DOI":"10.1145\/1656274.1656278"},{"key":"bibr43-20552076231184054","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2021.09.056"}],"container-title":["DIGITAL HEALTH"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/20552076231184054","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/20552076231184054","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/20552076231184054","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T17:53:35Z","timestamp":1777398815000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/20552076231184054"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1]]},"references-count":43,"alternative-id":["10.1177\/20552076231184054"],"URL":"https:\/\/doi.org\/10.1177\/20552076231184054","relation":{},"ISSN":["2055-2076","2055-2076"],"issn-type":[{"value":"2055-2076","type":"print"},{"value":"2055-2076","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1]]},"article-number":"20552076231184054"}}