Abstract
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, PM2.5, 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.
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Funding
This research was supported by the Fundamental Research Funds for the Central Universities (Grant No. buctrc202133), the National Natural Science Foundation of China (Grant No. 72174125), and the Environmental Health Risk Monitoring and Response Based on Big Data (Grant No. H2022006).
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Jing Yang: methodology, investigation, data curation, and writing—original draft. Xin Xu: methodology, investigation, data curation, and writing—original draft. Xiaotian Ma: investigation and methodology. Zhaotong Wang: data curation. Qian You: writing—review and editing. Wanyue Shan: investigation. Ying Yang: software. Xin Bo: conceptualization and writing–review and editing. Chuansheng Yin: conceptualization, methodology, and writing—review and editing. All the authors have read and agreed to the published version of the manuscript.
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Highlights
• Machine learning is used to examine correlations between meteorological and air pollution factors and outpatient visits.
• Pneumonia patient visits are strongly correlated with NO2, PM2.5, and PM10.
• The SVR model shows the best performance for pneumonia.
• The link between meteorological and air pollution factors and respiratory diseases is examined.
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Yang, J., Xu, X., Ma, X. et al. Application of machine learning to predict hospital visits for respiratory diseases using meteorological and air pollution factors in Linyi, China. Environ Sci Pollut Res 30, 88431–88443 (2023). https://doi.org/10.1007/s11356-023-28682-8
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DOI: https://doi.org/10.1007/s11356-023-28682-8