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

Songjing Chen; Sizhu Wu; Qing Qian; Jiao Li

doi:doi:10.11648/j.ijbse.20170503.11

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

Songjing Chen, Sizhu Wu, Qing Qian, Jiao Li

Published in International Journal of Biomedical Science and Engineering (Volume 5, Issue 3)

Received: 4 May 2017 Accepted: 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), 2024. Published by Science Publishing Group

Keywords

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

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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
PY - 2017
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

Songjing Chen

Institute of Medical Information, Chinese Academy of Medical Sciences, Beijing, China
Sizhu Wu

Institute of Medical Information, Chinese Academy of Medical Sciences, Beijing, China
Qing Qian

Institute of Medical Information, Chinese Academy of Medical Sciences, Beijing, China
Jiao Li

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

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Plain Text BibTeX RIS

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

Copy | Download

@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}
}

Copy | Download