Application of machine learning to predict hospital visits for respiratory diseases using meteorological and air pollution factors in Linyi, China

Urbanization and industrial development have resulted in increased air pollution, which is concerning for public health. This study evaluates the effect of meteorological factors and air pollution on hospital visits for respiratory diseases (pneumonia, acute upper respiratory infections, and chronic lower respiratory diseases). The test dataset comprises meteorological parameters, air pollutant concentrations, and outpatient hospital visits for respiratory diseases in Linyi, China, from January 1, 2016 to August 20, 2022. We use support vector regression (SVR) to build models that enable analysis of the effect of meteorological factors and air pollutants on the number of outpatient visits for respiratory diseases. Spearman correlation analysis and SVR model results indicate that NO₂, PM_2.5, and PM₁₀ are correlated with the occurrence of respiratory diseases, with the strongest correlation relating to pneumonia. An increase in the daily average temperature and daily relative humidity decreases the number of patients with pneumonia and chronic lower respiratory diseases but increases the number of patients with acute upper respiratory infections. The SVR modeling has the potential to predict the number of respiratory-related hospital visits. This work demonstrates that machine learning can be combined with meteorological and air pollution data for disease prediction, providing a useful tool whereby policymakers can take preventive measures.