Epidemiological association and machine learning-based prediction of lung cancer risk linked to long-term lagged satellite-derived PM2.5 in China
ObjectivesThis study investigated association between long-term PM2.5 exposure and lung cancer incidence, focusing on Jiangsu Province, China. We aimed to explore the effects of historical PM2.5 with time lags and build a prediction model using machine learning methods.Study designAn ecological epid...
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Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Public Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2025.1536509/full |
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| author | Feiran Wei Shijun Yang Huiying Wang Meng Zhao Meng Zhao Jinyi Zhou Xiaobing Shen Xiaobing Shen Renqiang Han Renqiang Han Gaoqiang Fei |
| author_facet | Feiran Wei Shijun Yang Huiying Wang Meng Zhao Meng Zhao Jinyi Zhou Xiaobing Shen Xiaobing Shen Renqiang Han Renqiang Han Gaoqiang Fei |
| author_sort | Feiran Wei |
| collection | DOAJ |
| description | ObjectivesThis study investigated association between long-term PM2.5 exposure and lung cancer incidence, focusing on Jiangsu Province, China. We aimed to explore the effects of historical PM2.5 with time lags and build a prediction model using machine learning methods.Study designAn ecological epidemiology study.MethodsLung cancer incidence data from Jiangsu Province (2014–2018) were combined with annual PM2.5 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 PM2.5 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.ResultsThe incidence of lung cancer was significantly correlated with PM2.5 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.ConclusionLong-term exposure to PM2.5, 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. |
| format | Article |
| id | doaj-art-a1c5b3f32e884bbfb8792e2c95006991 |
| institution | OA Journals |
| issn | 2296-2565 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj-art-a1c5b3f32e884bbfb8792e2c950069912025-08-20T02:17:08ZengFrontiers Media S.A.Frontiers in Public Health2296-25652025-05-011310.3389/fpubh.2025.15365091536509Epidemiological association and machine learning-based prediction of lung cancer risk linked to long-term lagged satellite-derived PM2.5 in ChinaFeiran Wei0Shijun Yang1Huiying Wang2Meng Zhao3Meng Zhao4Jinyi Zhou5Xiaobing Shen6Xiaobing Shen7Renqiang Han8Renqiang Han9Gaoqiang Fei10Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, ChinaGuangxi Meteorological Observatory, Nanjing, ChinaLianyungang Meteorological Bureau, Lianyungang, ChinaKey Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, ChinaDepartment of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing, ChinaJiangsu Provincial Center for Disease Control and Prevention (Jiangsu Provincial Academy of Preventive Medicine), Nanjing, ChinaKey Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, ChinaDepartment of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing, ChinaJiangsu Provincial Center for Disease Control and Prevention (Jiangsu Provincial Academy of Preventive Medicine), Nanjing, ChinaDepartment of Public Health, Jiangsu Cancer Hospital, The Affiliated Cancer Hospital of Nanjing Medical University, Jiangsu Institute of Cancer Research, Nanjing, ChinaDepartment of Public Health, Jiangsu Cancer Hospital, The Affiliated Cancer Hospital of Nanjing Medical University, Jiangsu Institute of Cancer Research, Nanjing, ChinaObjectivesThis study investigated association between long-term PM2.5 exposure and lung cancer incidence, focusing on Jiangsu Province, China. We aimed to explore the effects of historical PM2.5 with time lags and build a prediction model using machine learning methods.Study designAn ecological epidemiology study.MethodsLung cancer incidence data from Jiangsu Province (2014–2018) were combined with annual PM2.5 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 PM2.5 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.ResultsThe incidence of lung cancer was significantly correlated with PM2.5 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.ConclusionLong-term exposure to PM2.5, 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.https://www.frontiersin.org/articles/10.3389/fpubh.2025.1536509/fullPM2.5lung cancerlong-term exposuremachine learningprediction modelpublic health |
| spellingShingle | Feiran Wei Shijun Yang Huiying Wang Meng Zhao Meng Zhao Jinyi Zhou Xiaobing Shen Xiaobing Shen Renqiang Han Renqiang Han Gaoqiang Fei Epidemiological association and machine learning-based prediction of lung cancer risk linked to long-term lagged satellite-derived PM2.5 in China Frontiers in Public Health PM2.5 lung cancer long-term exposure machine learning prediction model public health |
| title | Epidemiological association and machine learning-based prediction of lung cancer risk linked to long-term lagged satellite-derived PM2.5 in China |
| title_full | Epidemiological association and machine learning-based prediction of lung cancer risk linked to long-term lagged satellite-derived PM2.5 in China |
| title_fullStr | Epidemiological association and machine learning-based prediction of lung cancer risk linked to long-term lagged satellite-derived PM2.5 in China |
| title_full_unstemmed | Epidemiological association and machine learning-based prediction of lung cancer risk linked to long-term lagged satellite-derived PM2.5 in China |
| title_short | Epidemiological association and machine learning-based prediction of lung cancer risk linked to long-term lagged satellite-derived PM2.5 in China |
| title_sort | epidemiological association and machine learning based prediction of lung cancer risk linked to long term lagged satellite derived pm2 5 in china |
| topic | PM2.5 lung cancer long-term exposure machine learning prediction model public health |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2025.1536509/full |
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