Prediction of Hospitalization Using Machine Learning for Emergency Department Patients
Georgios Feretzakis, Aikaterini Sakagianni, Evangelos Loupelis, Dimitris Kalles, Vasileios Panteris, Lazaros I. Tzelves, Rea Chatzikyriakou, Νικόλαος Τράκας, Stavroula Kolokytha, Polyxeni Batiani, Zoi Rakopoulou, Aikaterini Tika, Stavroula Petropoulou, Ilias Dalainas, Vasileios Kaldis · Studies in health technology and informatics · 2022
The objective of this study was to evaluate the predictive capability of five machine learning models regarding the admission or discharge of emergency department patients. A Random Forest classifier outperformed other models with respect to the area under the receiver operating characteristic curve (AUC ROC).