biostudies-literatureUnknown24(1)Porto BM<h4>Background</h4>Emergency department (ED) overcrowding is an important problem in many countries. Accurate predictions of ED patient arrivals can help management to better allocate staff and medical resources. In this study, we investigate the use of calendar and meteorological predictors, as well as feature-engineered variables, to predict daily patient arrivals using datasets from eleven different EDs across three countries.<h4>Methods</h4>Six machine learning (ML) algorithms were tested on forecasting horizons of 7 and 45 days. Three of them - Light Gradient Boosting Machine (LightGBM), Support Vector Machine with Radial Basis Function (SVM-RBF), and Neural Network Autoregression (NNAR) - were never before reported for predicting ED patient arrivals. Algorithms' hyperparameters were BMC medical informatics and decision making377https://www.ebi.ac.uk/biostudies/studies/S-EPMC11653554biostudies-literatureEnhanced forecasting of emergency department patient arrivals using feature engineering approach and machine learning.PMC11653554Porto BMFogliatto FSfalseEnhanced forecasting of emergency department patient arrivals using feature engineering approach and machine learning.<h4>Background</h4>Emergency department (ED) overcrowding is an important problem in many countries. Accurate predictions of ED patient arrivals can help management to better allocate staff and medical resources. In this study, we investigate the use of calendar and meteorological predictors, as well as feature-engineered variables, to predict daily patient arrivals using datasets from eleven different EDs across three countries.<h4>Methods</h4>Six machine learning (ML) algorithms were tested on forecasting horizons of 7 and 45 days. Three of them - Light Gradient Boosting Machine (LightGBM), Support Vector Machine with Radial Basis Function (SVM-RBF), and Neural Network Autoregression (NNAR) - were never before reported for predicting ED patient arrivals. Algorithms' hyperparameters were 2024-01-01T00:00:00Z2024 Dec2025-04-04T01:46:07.134Z2025-04-04T01:46:07.134ZS-EPMC116535543969622410.1186/s12911-024-02788-6