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Identifying Air Pollution Risk Factors for Respiratory Disease Using Quantitative Computational Method

Received: 4 May 2017     Published: 5 May 2017
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Abstract

In order to identify air pollution risk factors for respiratory disease patients, a quantitative computational method to identify high risk factors for respiratory patients was conducted in this study. The C4.5 classification algorithm was used in the computational method. SMOTE algorithm was applied to solve the imbalance data problem. Risk factor effect degree was calculated according to C4.5 classification model. Age was the top risk factor in nine subgroups, except ≤11. The age ≤49 youth were easier affected by NO2 and SO2 than >49. ≤49 were obviously more than >49. ≤49 were more easier suffer from acute upper respiratory infections, >49 were more easier suffer from influenza, pneumonia and chronic lower respiratory disease. The air pollution risk factors of respiratory disease were identified quantitatively. This quantitative computational method could be applied to predict other disease occurrence.

Published in International Journal of Biomedical Science and Engineering (Volume 5, Issue 3)
DOI 10.11648/j.ijbse.20170503.11
Page(s) 18-23
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

Respiratory Disease, Air Pollutant, C4.5 Classification Algorithm, Data Mining

References
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Cite This Article
  • APA Style

    Songjing Chen, Sizhu Wu, Qing Qian, Jiao Li. (2017). Identifying Air Pollution Risk Factors for Respiratory Disease Using Quantitative Computational Method. International Journal of Biomedical Science and Engineering, 5(3), 18-23. https://doi.org/10.11648/j.ijbse.20170503.11

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    ACS Style

    Songjing Chen; Sizhu Wu; Qing Qian; Jiao Li. Identifying Air Pollution Risk Factors for Respiratory Disease Using Quantitative Computational Method. Int. J. Biomed. Sci. Eng. 2017, 5(3), 18-23. doi: 10.11648/j.ijbse.20170503.11

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    AMA Style

    Songjing Chen, Sizhu Wu, Qing Qian, Jiao Li. Identifying Air Pollution Risk Factors for Respiratory Disease Using Quantitative Computational Method. Int J Biomed Sci Eng. 2017;5(3):18-23. doi: 10.11648/j.ijbse.20170503.11

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  • @article{10.11648/j.ijbse.20170503.11,
      author = {Songjing Chen and Sizhu Wu and Qing Qian and Jiao Li},
      title = {Identifying Air Pollution Risk Factors for Respiratory Disease Using Quantitative Computational Method},
      journal = {International Journal of Biomedical Science and Engineering},
      volume = {5},
      number = {3},
      pages = {18-23},
      doi = {10.11648/j.ijbse.20170503.11},
      url = {https://doi.org/10.11648/j.ijbse.20170503.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijbse.20170503.11},
      abstract = {In order to identify air pollution risk factors for respiratory disease patients, a quantitative computational method to identify high risk factors for respiratory patients was conducted in this study. The C4.5 classification algorithm was used in the computational method. SMOTE algorithm was applied to solve the imbalance data problem. Risk factor effect degree was calculated according to C4.5 classification model. Age was the top risk factor in nine subgroups, except ≤11. The age ≤49 youth were easier affected by NO2 and SO2 than >49. ≤49 were obviously more than >49. ≤49 were more easier suffer from acute upper respiratory infections, >49 were more easier suffer from influenza, pneumonia and chronic lower respiratory disease. The air pollution risk factors of respiratory disease were identified quantitatively. This quantitative computational method could be applied to predict other disease occurrence.},
     year = {2017}
    }
    

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  • TY  - JOUR
    T1  - Identifying Air Pollution Risk Factors for Respiratory Disease Using Quantitative Computational Method
    AU  - Songjing Chen
    AU  - Sizhu Wu
    AU  - Qing Qian
    AU  - Jiao Li
    Y1  - 2017/05/05
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    N1  - https://doi.org/10.11648/j.ijbse.20170503.11
    DO  - 10.11648/j.ijbse.20170503.11
    T2  - International Journal of Biomedical Science and Engineering
    JF  - International Journal of Biomedical Science and Engineering
    JO  - International Journal of Biomedical Science and Engineering
    SP  - 18
    EP  - 23
    PB  - Science Publishing Group
    SN  - 2376-7235
    UR  - https://doi.org/10.11648/j.ijbse.20170503.11
    AB  - In order to identify air pollution risk factors for respiratory disease patients, a quantitative computational method to identify high risk factors for respiratory patients was conducted in this study. The C4.5 classification algorithm was used in the computational method. SMOTE algorithm was applied to solve the imbalance data problem. Risk factor effect degree was calculated according to C4.5 classification model. Age was the top risk factor in nine subgroups, except ≤11. The age ≤49 youth were easier affected by NO2 and SO2 than >49. ≤49 were obviously more than >49. ≤49 were more easier suffer from acute upper respiratory infections, >49 were more easier suffer from influenza, pneumonia and chronic lower respiratory disease. The air pollution risk factors of respiratory disease were identified quantitatively. This quantitative computational method could be applied to predict other disease occurrence.
    VL  - 5
    IS  - 3
    ER  - 

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Author Information
  • Institute of Medical Information, Chinese Academy of Medical Sciences, Beijing, China

  • Institute of Medical Information, Chinese Academy of Medical Sciences, Beijing, China

  • Institute of Medical Information, Chinese Academy of Medical Sciences, Beijing, China

  • Institute of Medical Information, Chinese Academy of Medical Sciences, Beijing, China

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