Regression analysis of air pollution and pediatric respiratory diseases based on interpretable machine learning
نویسندگان
چکیده
Air pollution is of high relevance to human health. In this study, multiple machine-learning (ML) models—linear regression, random forest (RF), AdaBoost, and neural networks (NNs)—were used explore the potential impacts air-pollutant concentrations on incidence pediatric respiratory diseases in Taizhou, China. A number explainable artificial intelligence (XAI) methods were further applied analyze model outputs quantify feature importance. Our results demonstrate that there are significant seasonal variations both numbers outpatients air pollutants. The NO 2 , CO, particulate matter (PM 10 PM 2.5 ), as well outpatients, reach their peak values winter. This indicates a major factor diseases. regression models show ML can capture trends turning points clinic visits, non-linear superior linear ones. Among them, RF served best-performing model. analysis by XAI found AQI, O 3 current month most important predictors affecting outpatients. shows rises with an increasing especially matter. study promising for revealing underlying diseases, which assists health-related decision-making.
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ژورنال
عنوان ژورنال: Frontiers in Earth Science
سال: 2023
ISSN: ['2296-6463']
DOI: https://doi.org/10.3389/feart.2023.1105140