Machine Learning Models for Predicting Pediatric Hospitalizations Due to Air Pollution and Humidity: A Retrospective Study.

Abstract

Background: Exposure to air pollution and meteorological conditions, such as humidity, has been linked to adverse respiratory health outcomes in children. This study aims to develop predictive models for pediatric hospitalizations based on both environmental exposures and clinical features.

Methods: We conducted a retrospective analysis of 2500 children (aged 1-18) who presented with respiratory symptoms at the emergency department, during 2016-2017. Air pollution data, including NOx and NO₂ concentrations, and relative humidity (RH) were collected from nine monitoring stations and were cross-referenced with the children's residential locations to assess their specific exposure level. Statistical tests, including Chi-square and Wilcoxon tests, were used to analyze the data. Machine learning models, specifically Random Forest (RF) and eXtreme Gradient Boosting (XGBoost), were developed to predict the children's hospitalizations.

Results: Boys were more likely to be hospitalized than girls (60.6% vs. 39.4%, p = 4.31e-06). Hospital visits peaked during winter (p = 3.56e-37). Increased emergency room visits were statistically significantly associated with highly polluted days (p = 0.038). Hospitalized children were exposed to lower RH (median 64.9%) compared to nonhospitalized children (median 69.4%, p = 0.005). The RF and XGBoost models were reliable, with accuracy rates of 0.7-0.98, Precision scores of 0.88-0.99, and AUC scores of 81%-99%. Key features included temperature, NOx levels, RH, and exposure to SO₂.

Conclusion: This study investigates the effects of air pollution and humidity on pediatric respiratory health. The models developed offer valuable tools for predicting hospitalizations and are intended to support public health planning and resource allocation.