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ORIGINAL RESEARCH article
Front. Public Health
Sec. Environmental Health and Exposome
Volume 13 - 2025 |
doi: 10.3389/fpubh.2025.1537238
This article is part of the Research Topic Impact of Environmental Factors on the Health of Children and Older Adults View all 5 articles
Prediction of Respiratory Diseases based on Random Forest Model
Provisionally accepted- School of Meteorology and Oceanography, National University of Defense Technology, Changsha, China
In recent years, the random forest model has been widely applied to analyze the relationships among air pollution, meteorological factors, and human health. To investigate the patterns and influencing factors of respiratory disease-related medical visits, this study utilized data on medical visits from urban areas of Tianjin, meteorological observations, and pollution data. First, the temporal variation characteristics of medical visits from 2013 to 2019 were analyzed. Subsequently, the random forest model was employed to identify the dominant influencing factors of respiratory disease-related medical visits and to construct a statistical forecasting model that relates these factors to the number of visits. Additionally, a predictive analysis of medical visits in Tianjin for the year 2019 was conducted. The results indicate the following: (1) From 2013 to 2019, the number of medical visits exhibited seasonal fluctuations, with a significant decline observed in 2017, which may be directly related to adjustments in hospital policies. (2) Among the meteorological factors, average temperature, relative humidity, precipitation, and ozone concentration significantly influenced the variation in medical visits, while wind speed, precipitation amount, and boundary layer height were of lesser importance. Furthermore, different linear relationships exist among the meteorological factors; specifically, meteorological factors show a negative correlation with pollutant elements, and there is a strong correlation among the pollutant factors. (3) When the number of medical visits ranged from 50 to 200, the predictions made by the random forest model closely matched the actual values, demonstrating strong predictive performance and the ability to effectively forecast daily variations in medical visits over extended periods, thus exhibiting good stability and generalization capability. (4) However, since the random forest model relies on a large amount of data for model validation, it has limitations in capturing extreme variations in medical visit numbers. Future research
Keywords: random forest 1, prediction 2, meteorological factors 3, human health4, pollutant5
Received: 30 Nov 2024; Accepted: 20 Jan 2025.
Copyright: © 2025 Xiaotong, Liu, Li and Zang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Yang Xiaotong, School of Meteorology and Oceanography, National University of Defense Technology, Changsha, China
Lang Liu, School of Meteorology and Oceanography, National University of Defense Technology, Changsha, China
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