The forecasting of pediatric asthma clinic visits: A comparative analysis of time-series models under varying training set sizes

Objective This study aimed to identify the optimal time-series models and training strategies for forecasting daily pediatric asthma visit volumes and explore the impact of varying training set sizes on model performance to provide a data-driven framework for clinical resource allocation. Methods This paper compares the performance of four representative time-series models (autoregressive integrated moving average, Prophet, extreme gradient boosting, bidirectional long short-term memory) in forecasting pediatric asthma daily visits and investigates the impact of varying training set sizes on model performance. A retrospective study was conducted using daily pediatric asthma visit data from July 1, 2015, to June 30, 2019, at a large tertiary children's hospital in Chongqing, China. Four representative time-series models were constructed and evaluated under two forecasting strategies (rolling and direct forecasting) with varying training set sizes (3 years, 2 years, 1 year, 6 months, 1 month). The models were evaluated using metrics including the coefficient of determination R 2 , mean absolute error, root mean squared error, and days exceeding error thresholds. Results The experimental results indicate that extreme gradient boosting and bidirectional long short-term memory are reliable for pediatric asthma visit forecasting, with 2-year training data and rolling forecasting optimal. Ensemble method combining the above two models reduced error days compared to single models. Conclusion This research presents a robust framework for hospitals to implement data-driven forecasting of pediatric asthma visit volumes, integrating machine learning models and deep learning model with adaptive training strategies to improve the efficiency of resource management in this clinical domain.