Forecasting Emergency Room Patient Volumes Using Extreme Gradient Boosting With Temporal and Seasonal Feature Engineering: A Comparative Study Across Hospitals

Leveraging temporal and historical patterns in simulated ED data, the XGBoost model delivers markedly more accurate volume forecasts than traditional baseline methods. These findings on synthetic data support the potential for machine learning-based forecasting models in enhancing hospital operational decision-making, with future directions involving the use of real-world hospital data.