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To address these challenges, we propose a novel Meta-Learning Gradient Booster (Meta-ED) approach to forecast daily ED visits. Meta-ED leverages a comprehensive dataset spanning 23\u2009years from Canberra Hospital, incorporating exogenous variables such as socio-demographic characteristics, healthcare usage, chronic diseases, diagnoses and climate parameters. Meta-ED combines four foundational learners\u2014CatBoost, Random Forest, Extra Trees and LightGBM\u2014with a Multi-Layer Perceptron (MLP) as the master-level learner, thereby enhancing predictive precision by integrating the strengths of diverse base models. Our comparative analysis, which involved testing 23 models against a set of predefined criteria, demonstrates the superior performance of Meta-ED, achieving an accuracy of 85.7% (95% CI [85.4%, 86.0%]) and outperforming prominent models like XGBoost, Random Forest, AdaBoost, LightGBM and Extra Trees by up to 106.3%. Furthermore, incorporating climate features resulted in a 3.25% improvement in prediction accuracy, effectively capturing seasonal variations that influence patient volumes. These results underscore the potential of Meta-ED to advance predictive analytics in complex healthcare environments.<\/jats:p>","DOI":"10.1145\/3768317","type":"journal-article","created":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T18:43:51Z","timestamp":1758221031000},"page":"1-33","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Effective Predictive Modelling for Emergency Department Visits and Evaluating Exogenous Variables Impact: Using Explainable Meta-Learning Gradient Boosting"],"prefix":"10.1145","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9537-9513","authenticated-orcid":false,"given":"Mehdi","family":"Neshat","sequence":"first","affiliation":[{"name":"Canberra Health Services, Canberra, Australia and University of Technology Sydney, Sydney, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8800-1862","authenticated-orcid":false,"given":"Michael","family":"Phipps","sequence":"additional","affiliation":[{"name":"Canberra Health Services, Canberra Hospital, Canberra, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-9640-2062","authenticated-orcid":false,"given":"Nikhil","family":"Jha","sequence":"additional","affiliation":[{"name":"Canberra Health Services, Canberra Hospital, Canberra, Australia and School of Medicine and Psychology, The Australian National University, Garran, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6095-2885","authenticated-orcid":false,"given":"Danial","family":"Khojasteh","sequence":"additional","affiliation":[{"name":"School of Civil and Environmental Engineering, University of New South Wales, Sydney, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9694-9207","authenticated-orcid":false,"given":"Michael","family":"Tong","sequence":"additional","affiliation":[{"name":"National Centre for Epidemiology and Population Health, ANU College of Health and Medicine, The Australian National University, Canberra, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2798-0104","authenticated-orcid":false,"given":"Amir H.","family":"Gandomi","sequence":"additional","affiliation":[{"name":"Faculty of Engineering and Information Technology, University of Technology Sydney, Australia University Research and Innovation Center (EKIK), Sydney, Australia, Obuda University, Budapest, Hungary, and Department of Computer Science, Khazar University, Baku, Azerbaijan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,1,14]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2015.06.022"},{"key":"e_1_3_3_3_2","unstructured":"ACT Government. 2023. 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