{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T07:44:44Z","timestamp":1782373484607,"version":"3.54.5"},"reference-count":25,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,12,27]],"date-time":"2021-12-27T00:00:00Z","timestamp":1640563200000},"content-version":"vor","delay-in-days":360,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004054","name":"King Abdulaziz University","doi-asserted-by":"publisher","award":["KEP-PhD-48-130-38"],"award-info":[{"award-number":["KEP-PhD-48-130-38"]}],"id":[{"id":"10.13039\/501100004054","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>Recently, Internet of Things (IoT) and cloud computing environments become commonly employed in several healthcare applications by the integration of monitoring things such as sensors and medical gadgets for observing remote patients. For availing of improved healthcare services, the huge count of data generated by IoT gadgets from the medicinal field can be investigated in the CC environment rather than relying on limited processing and storage resources. At the same time, earlier identification of chronic kidney disease (CKD) becomes essential to reduce the mortality rate significantly. This study develops an ensemble of deep learning based clinical decision support systems (EDL\u2010CDSS) for CKD diagnosis in the IoT environment. The goal of the EDL\u2010CDSS technique is to detect and classify different stages of CKD using the medical data collected by IoT devices and benchmark repositories. In addition, the EDL\u2010CDSS technique involves the design of Adaptive Synthetic (ADASYN) technique for outlier detection process. Moreover, an ensemble of three models, namely, deep belief network (DBN), kernel extreme learning machine (KELM), and convolutional neural network with gated recurrent unit (CNN\u2010GRU), are performed. Finally, quasi\u2010oppositional butterfly optimization algorithm (QOBOA) is used for the hyperparameter tuning of the DBN and CNN\u2010GRU models. A wide range of simulations was carried out and the outcomes are studied in terms of distinct measures. A brief outcomes analysis highlighted the supremacy of the EDL\u2010CDSS technique on exiting approaches.<\/jats:p>","DOI":"10.1155\/2021\/4931450","type":"journal-article","created":{"date-parts":[[2021,12,28]],"date-time":"2021-12-28T00:05:55Z","timestamp":1640649955000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":57,"title":["Ensemble of Deep Learning Based Clinical Decision Support System for Chronic Kidney Disease Diagnosis in Medical Internet of Things Environment"],"prefix":"10.1155","volume":"2021","author":[{"given":"Suliman A.","family":"Alsuhibany","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sayed","family":"Abdel-Khalek","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali","family":"Algarni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aisha","family":"Fayomi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3019-7161","authenticated-orcid":false,"given":"Deepak","family":"Gupta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vinay","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5857-8495","authenticated-orcid":false,"given":"Romany F.","family":"Mansour","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,12,27]]},"reference":[{"key":"e_1_2_12_1_2","doi-asserted-by":"crossref","unstructured":"VeluK. ArulanthuP. andPerumalE. Energy reduction stratagem in smart homes using association rule mining Proceedings of the International Conference on Innovative Data Communication Technologies and Application Springer 2019 Cham Switzerland 188\u2013193 https:\/\/doi.org\/10.1007\/978-3-030-38040-3_22.","DOI":"10.1007\/978-3-030-38040-3_22"},{"key":"e_1_2_12_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2010.05.010"},{"key":"e_1_2_12_3_2","first-page":"2250","article-title":"An effect of machine learning based classification algorithms on chronic kidney disease","volume":"9","author":"Lambert J. R.","year":"2020","journal-title":"International Journal of Innovative Technology Exploring Engineering"},{"key":"e_1_2_12_4_2","doi-asserted-by":"publisher","DOI":"10.3390\/s18124307"},{"key":"e_1_2_12_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2019.01.070"},{"key":"e_1_2_12_6_2","doi-asserted-by":"publisher","DOI":"10.1002\/ett.3637"},{"key":"e_1_2_12_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-019-7327-8"},{"key":"e_1_2_12_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2015.08.041"},{"key":"e_1_2_12_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2019.10.043"},{"key":"e_1_2_12_10_2","doi-asserted-by":"crossref","unstructured":"HamimM. PaulS. HoqueS. I. RahmanN. andBaqeeI. A. IoT based remote health monitoring system for patients and elderly people Proceedings of the 2019 International Conference on Robotics Electrical and Signal Processing Techniques (ICREST) January 2019 Dhaka Bangladesh IEEE https:\/\/doi.org\/10.1109\/icrest.2019.8644514 2-s2.0-85063103698.","DOI":"10.1109\/ICREST.2019.8644514"},{"key":"e_1_2_12_11_2","unstructured":"ShrivasA. SahuS. K. andHotaH. Classification of chronic kidney disease with proposed union based feature selection technique Proceedings of the 3rd International Conference on Internet of Things and Connected Technologies (ICIoTCT) 2018 India March."},{"key":"e_1_2_12_12_2","doi-asserted-by":"publisher","DOI":"10.21203\/rs.3.rs-380904\/v1"},{"key":"e_1_2_12_13_2","doi-asserted-by":"crossref","unstructured":"NoorA. BanerjeeA. AhmadM. F. andUddinM. N. An IoT based mhealth platform for chronic kidney disease patients Proceedings of the 2019 1st International Conference on Advances in Science Engineering and Robotics Technology (ICASERT) May 2019 Dhaka Bangladesh IEEE 1\u20136.","DOI":"10.1109\/ICASERT.2019.8934565"},{"key":"e_1_2_12_14_2","doi-asserted-by":"publisher","DOI":"10.1002\/ima.22424"},{"key":"e_1_2_12_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/lsens.2019.2942145"},{"key":"e_1_2_12_16_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01560-2_5"},{"key":"e_1_2_12_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2020.04.036"},{"key":"e_1_2_12_18_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-020-09049-4"},{"key":"e_1_2_12_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bbe.2018.06.009"},{"key":"e_1_2_12_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2021.3112478"},{"key":"e_1_2_12_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2021.3051074"},{"key":"e_1_2_12_22_2","doi-asserted-by":"crossref","unstructured":"NguyenV. CaiJ. andChuJ. Hybrid CNN-GRU model for high efficient handwritten digit recognition Proceedings of the 2nd International Conference on Artificial Intelligence and Pattern Recognition August 2019 Beijing China 66\u201371.","DOI":"10.1145\/3357254.3357276"},{"key":"e_1_2_12_23_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-018-3102-4"},{"key":"e_1_2_12_24_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2020.04.209"},{"key":"e_1_2_12_25_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-46074-2"}],"container-title":["Computational Intelligence and Neuroscience"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2021\/4931450.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2021\/4931450.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2021\/4931450","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,6]],"date-time":"2024-08-06T11:55:11Z","timestamp":1722945311000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2021\/4931450"}},"subtitle":[],"editor":[{"given":"Ahmed A.","family":"Abd El-Latif","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2021,1]]},"references-count":25,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["10.1155\/2021\/4931450"],"URL":"https:\/\/doi.org\/10.1155\/2021\/4931450","archive":["Portico"],"relation":{},"ISSN":["1687-5265","1687-5273"],"issn-type":[{"value":"1687-5265","type":"print"},{"value":"1687-5273","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1]]},"assertion":[{"value":"2021-11-15","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-12-16","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-12-27","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"4931450"}}