{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T10:01:07Z","timestamp":1766138467792,"version":"3.40.5"},"reference-count":27,"publisher":"Wiley","license":[{"start":{"date-parts":[[2023,10,27]],"date-time":"2023-10-27T00:00:00Z","timestamp":1698364800000},"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":[[2023,10,27]]},"abstract":"<jats:p>As the COVID-19 pandemic has affected the globe, health systems worldwide have also been significantly affected. This pandemic has impacted many sectors, including health in the Kingdom of Jordan. Crises that put heavy pressure on the health systems\u2019 shoulders include the emergency departments (ED), the most demanded hospital resources during normal conditions, and critical during crises. However, managing the health systems efficiently and achieving the best planning and allocation of their EDs\u2019 resources becomes crucial to improve their capabilities to accommodate the crisis\u2019s impact. Knowing critical factors affecting the patient length of stay prediction is critical to reducing the risks of prolonged waiting and clustering inside EDs. That is, by focusing on these factors and analyzing the effect of each. This research aims to determine the critical factors that predict the outcome: the length of stay, i.e., the predictor variables. Therefore, patients\u2019 length of stay in EDs across waiting time duration is categorized as (low, medium, and high) using supervised machine learning (ML) approaches. Unsupervised algorithms have been applied to classify the patient\u2019s length of stay in local EDs in the Kingdom of Jordan. The Arab Medical Centre Hospital is selected as a case study to justify the performance of the proposed ML model. Data that spans a time interval of 22\u2009months, covering the period before and after COVID-19, is used to train the proposed feedforward network. The proposed model is compared with other ML approaches to justify its superiority. Also, comparative and correlation analyses are conducted on the considered attributes (inputs) to help classify the LOS and the patient\u2019s length of stay in the ED. The best algorithms to be used are the trees such as the decision stump, REB tree, and Random Forest and the multilayer perceptron (with batch sizes of 50 and 0.001 learning rate) for this specific problem. Results showed better performance in terms of accuracy and easiness of implementation.<\/jats:p>","DOI":"10.1155\/2023\/8063846","type":"journal-article","created":{"date-parts":[[2023,10,27]],"date-time":"2023-10-27T20:35:09Z","timestamp":1698438909000},"page":"1-13","source":"Crossref","is-referenced-by-count":5,"title":["Machine Learning Approaches to Predict Patient\u2019s Length of Stay in Emergency Department"],"prefix":"10.1155","volume":"2023","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9413-7985","authenticated-orcid":true,"given":"Mohammad A.","family":"Shbool","sequence":"first","affiliation":[{"name":"Industrial Engineering Department, School of Engineering, The University of Jordan, Amman 11942, Jordan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6590-1211","authenticated-orcid":true,"given":"Omar","family":"S. Arabeyyat","sequence":"additional","affiliation":[{"name":"Project Management Department, Faculty of Business, Al-Balqa Applied University, Al-Salt 19117, Jordan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5057-4171","authenticated-orcid":true,"given":"Ammar","family":"Al-Bazi","sequence":"additional","affiliation":[{"name":"Aston Business School, Aston University, Birmingham B4 7ER, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0295-9691","authenticated-orcid":true,"given":"Abeer","family":"Al-Hyari","sequence":"additional","affiliation":[{"name":"Computer Engineering Department, Faculty of Engineering, Al-Balqa Applied University, Al-Salt 19117, Jordan"}]},{"given":"Arwa","family":"Salem","sequence":"additional","affiliation":[{"name":"Industrial Engineering Department, School of Engineering, The University of Jordan, Amman 11942, Jordan"}]},{"given":"Thana\u2019","family":"Abu-Hmaid","sequence":"additional","affiliation":[{"name":"Industrial Engineering Department, School of Engineering, The University of Jordan, Amman 11942, Jordan"}]},{"given":"Malak","family":"Ali","sequence":"additional","affiliation":[{"name":"Industrial Engineering Department, School of Engineering, The University of Jordan, Amman 11942, Jordan"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1111\/acem.12654"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijid.2020.06.105"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0165756"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1109\/3ICT56508.2022.9990827"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1155\/2013\/312348"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/6612187"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/9517029"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsx.2020.04.012"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.1017\/S1481803500006539"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.2196\/ijmr.4022"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.1136\/bmjinnov-2020-000420"},{"key":"12","doi-asserted-by":"publisher","DOI":"10.1111\/eci.13406"},{"key":"13","doi-asserted-by":"publisher","DOI":"10.1016\/j.asej.2021.02.018"},{"key":"14","doi-asserted-by":"publisher","DOI":"10.1097\/00063110-200410000-00004"},{"key":"15","doi-asserted-by":"publisher","DOI":"10.1111\/acem.12755"},{"key":"16","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/3783058"},{"key":"17","doi-asserted-by":"publisher","DOI":"10.7603\/s40690-015-0015-7"},{"key":"18","doi-asserted-by":"publisher","DOI":"10.1111\/acem.12876"},{"key":"19","doi-asserted-by":"publisher","DOI":"10.1097\/MEJ.0000000000000452"},{"key":"20","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/3366879"},{"key":"21","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3168045"},{"key":"22","doi-asserted-by":"publisher","DOI":"10.1109\/BigData55660.2022.10020253"},{"key":"23","doi-asserted-by":"publisher","DOI":"10.1111\/1742-6723.13421"},{"key":"24","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2015.12.006"},{"article-title":"Unsupervised machine learning: clustering analysis","year":"2019","author":"V. 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