Epidemiological association and machine learning-based prediction of lung cancer risk linked to long-term lagged satellite-derived PM2.5 in China

Objectives This study investigated association between long-term PM 2.5 exposure and lung cancer incidence, focusing on Jiangsu Province, China. We aimed to explore the effects of historical PM 2.5 with time lags and build a prediction model using machine learning methods. Study design An ecological epidemiology study. Methods Lung cancer incidence data from Jiangsu Province (2014–2018) were combined with annual PM 2.5 concentration data from satellite sources for the previous 10 years (lag 0 to lag 9). Correlation and grey correlation analyses were performed to evaluate the lagged relationship between PM 2.5 exposure and lung cancer incidence. To address the multicollinearity problem in the data, ridge regression, support vector regression, and back propagation artificial neural network were employed. The combined prediction model was constructed using the optimal weighting method. Results The incidence of lung cancer was significantly correlated with PM 2.5 concentration at different historical time points, with the strongest correlation at lag 9. The combined prediction model that integrates multiple prediction methods showed higher accuracy and reliability in predicting lung cancer incidence than a single model. Conclusion Long-term exposure to PM 2.5, especially exposure with a long lag time, is closely related to lung cancer incidence. The integrated machine learning prediction model can be used as a reliable tool to assess the health risks of air pollution

China · Environmental health · Geography · Lung cancer · Pathology · Satellite · Advanced Technologies in Various Fields · Air Quality and Health Impacts · Air Quality Monitoring and Forecasting · Computer Science · Engineering · Medicine · Psychology · Artificial Intelligence · Epidemiology · Oncology

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