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How Is the Lung Cancer Incidence Rate Associated with Environmental Risks? Machine-Learning-Based Modeling and Benchmarking

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  • Kung-Min Wang

    (Department of Industrial Management, National Taiwan University of Science and Technology, Taipei 106, Taiwan)

  • Kun-Huang Chen

    (College of Management and Design, Ming-Chi University of Technology, Taipei 243, Taiwan)

  • Chrestella Ayu Hernanda

    (Department of Industrial Management, National Taiwan University of Science and Technology, Taipei 106, Taiwan)

  • Shih-Hsien Tseng

    (Department of Industrial Management, National Taiwan University of Science and Technology, Taipei 106, Taiwan)

  • Kung-Jeng Wang

    (Department of Industrial Management, National Taiwan University of Science and Technology, Taipei 106, Taiwan)

Abstract

The lung cancer threat has become a critical issue for public health. Research has been devoted to its clinical study but only a few studies have addressed the issue from a holistic perspective that included social, economic, and environmental dimensions. Therefore, in this study, risk factors or features, such as air pollution, tobacco use, socioeconomic status, employment status, marital status, and environment, were comprehensively considered when constructing a predictive model. These risk factors were analyzed and selected using stepwise regression and the variance inflation factor to eliminate the possibility of multicollinearity. To build efficient and informative prediction models of lung cancer incidence rates, several machine learning algorithms with cross-validation were adopted, namely, linear regression, support vector regression, random forest, K-nearest neighbor, and cubist model tree. A case study in Taiwan showed that the cubist model tree with feature selection was the best model with an RMSE of 3.310 and an R-squared of 0.960. Through these predictive models, we also found that apart from smoking, the average NO 2 concentration, employment percentage, and number of factories were also important factors that had significant impacts on the incidence of lung cancer. In addition, the random forest model without feature selection and with feature selection could support the interpretation of the most contributing variables. The predictive model proposed in the present study can help to precisely analyze and estimate lung cancer incidence rates so that effective preventative measures can be developed. Furthermore, the risk factors involved in the predictive model can help with the future analysis of lung cancer incidence rates from a holistic perspective.

Suggested Citation

  • Kung-Min Wang & Kun-Huang Chen & Chrestella Ayu Hernanda & Shih-Hsien Tseng & Kung-Jeng Wang, 2022. "How Is the Lung Cancer Incidence Rate Associated with Environmental Risks? Machine-Learning-Based Modeling and Benchmarking," IJERPH, MDPI, vol. 19(14), pages 1-19, July.
  • Handle: RePEc:gam:jijerp:v:19:y:2022:i:14:p:8445-:d:859855
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    References listed on IDEAS

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    1. Hong-Bae Kim & Jae-Yong Shim & Byoungjin Park & Yong-Jae Lee, 2018. "Long-Term Exposure to Air Pollutants and Cancer Mortality: A Meta-Analysis of Cohort Studies," IJERPH, MDPI, vol. 15(11), pages 1-15, November.
    2. Kung-Jeng Wang & Chia-Min Lee & Gwo-Chi Hu & Kung-Min Wang, 2020. "Stroke to Dementia Associated with Environmental Risks—A Semi-Markov Model," IJERPH, MDPI, vol. 17(6), pages 1-13, March.
    3. Da Hye Moon & Sung Ok Kwon & Sun-Young Kim & Woo Jin Kim, 2020. "Air Pollution and Incidence of Lung Cancer by Histological Type in Korean Adults: A Korean National Health Insurance Service Health Examinee Cohort Study," IJERPH, MDPI, vol. 17(3), pages 1-11, February.
    4. Stayner, L. & Bena, J. & Sasco, A.J. & Smith, R. & Steenland, K. & Kreuzer, M. & Straif, K., 2007. "Lung cancer risk and workplace exposure to environmental tobacco smoke," American Journal of Public Health, American Public Health Association, vol. 97(3), pages 545-551.
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