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The experiments were carried out on a real database collected from the pediatric emergency department (PED) in Lille regional hospital center, France. Different machine learning techniques have been used to build the best prediction models. The results seem better with Naive Bayes, C4.5 and SVM methods. In addition, the models based on a subset of attributes proved to be more efficient than models based on the set of attributes.<\/p>","DOI":"10.4018\/ijisss.2019070102","type":"journal-article","created":{"date-parts":[[2019,5,28]],"date-time":"2019-05-28T21:16:56Z","timestamp":1559078216000},"page":"19-36","source":"Crossref","is-referenced-by-count":11,"title":["Machine Learning for Emergency Department Management"],"prefix":"10.4018","volume":"11","author":[{"given":"Sofia","family":"Benbelkacem","sequence":"first","affiliation":[{"name":"Laboratoire d'Informatique d'Oran (LIO), University of Oran 1 Ahmed Ben Bella, Algeria"}]},{"given":"Farid","family":"Kadri","sequence":"additional","affiliation":[{"name":"Big Data & Analytics Services, Institut d'Optique Graduate School, Talence, France"}]},{"given":"Baghdad","family":"Atmani","sequence":"additional","affiliation":[{"name":"Laboratoire d'Informatique d'Oran (LIO), University of Oran 1 Ahmed Ben Bella, Algeria"}]},{"given":"Sond\u00e8s","family":"Chaabane","sequence":"additional","affiliation":[{"name":"University Polytechnique Hauts-de-France, CNRS, UMR 8201 \u2013 LAMIH, Laboratoire d'Automatique de M\u00e9canique et d'Informatique Industrielles et Humaines, F-59313 Valenciennes, France"}]}],"member":"2432","reference":[{"key":"IJISSS.2019070102-0","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-014-0029-x"},{"key":"IJISSS.2019070102-1","doi-asserted-by":"crossref","unstructured":"Azari, A., Janeja, V. 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