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

| Peer-Reviewed

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 Published: 5 May 2017

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

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

[1]	Klea K, Jonathan M. Air pollution and health: A European and North American Approach (APHENA). Health Effects Institute Research Report, 142, 5-90(2009).
[2]	Alexandros G, Bertil F, Klea K, et al. Acute effects of ozone on mortality from the “Air Pollution and Health: A European Approach” Project. American Journal of Respiratory and Critical Care Medicine. 170(10), 1080-1087 (2004).
[3]	Evangelia S, Roger P, Tim R, et al. Acute effects of ambient particulate matter on mortality in Europe and North America: Results from the APHENA Study. Environmental Health Perspectives. 116(11), 1480-1486 (2008).
[4]	Dominici F, McDermott A, Daniels M, et al. Revised analyses of the national morbidity, mortality, and air pollution study: Mortality among residents of 90 cities. Journal of Toxicology and Environmental Health. 68(13), 1071-1092 (2005).
[5]	Kan H, Chen B, Zhao N, et al. Part 1. A time-series study of ambient air pollution and daily mortality in Shanghai, China. Health Effects Institute Research Report. 154, (2010)17-78.
[6]	Nitesh V, Kevin W, Lawrence O, et al. SMOTE: Synthetic Minority Over-sampling technique. Journal of Artificial Intelligence Research. 12(6), 321-357 (2002).
[7]	Nakamura M, Kajiwara Y, Otsuka A, et al. LVQ-SMOTE - Learning vector quantization based Synthetic Minority Over-sampling Technique for biomedical data. Biodata Mining. 6(16), 1-10 (2013).
[8]	Dai, HL. Class imbalance learning via a fuzzy total margin based support vector machine. Applied Soft Computing. 31(1), 172-184(2015).
[9]	ICD-10 Version:2010 http://apps.who.int/classifications/icd10/browse/2010/en.
[10]	Anoop S, Jeremy P, Harish N, et al. Global association of air pollution and heart failure: a systematic review and meta-analysis. Lancet. 382(9), 1039-1048(2013).
[11]	Mustafic H, Jabre P, Caussin C, et al. Main air pollutants and myocardial infarction: a systematic review and meta-analysis. The Journal of the American Medical Association. 307(7), 713–721 (2012).
[12]	Duval S, Tweedie R. Trim and fill: a simple funnel-plot-based method of testing and adjusting for publication bias in meta-analysis. Biometrics. 56(2), 455–463(2000).
[13]	Majid E, Stephen V, Anthony R, et al. Estimates of global and regional potential health gains from reducing multiple major risk factors. Lancet. 362(7), 271-280(2003).
[14]	Nicos M, Panayiotis Y, Savvas K, et al. A 10-year time-series analysis of respiratory and cardiovascular morbidity in Nicosia, Cyprus: the effect of short-term changes in air pollution and dust storms. Environmental Health. 39(7), 1-16(2008).
[15]	Shan Zheng, Minzhen Wang, Shigong Wang, et al. Short-Term effects of gaseous pollutants and particulate matter on daily hospital admissions for Cardio-Cerebrovascular disease in Lanzhou: Evidence from a heavily polluted city in China. International Journal of Environmental Research and Public Health. 10(2), 462-477(2013).
[16]	Monica C, Ennio C, Massimo S, et al. Short-Term effects of Nitrogen Dioxide on mortality and susceptibility factors in 10 Italian cities: The EpiAir Study. Environmental Health Perspectives. 119(11), 1233-1238(2011).
[17]	Colais P, Faustini A, Stafoggia M, et al. Particulate air pollution and hospital admissions for cardiac diseases in potentially sensitive subgroups. Epidemiology. 23(3), 473–481(2012).
[18]	Peng RD, Samoli E, Pham L, et al. Acute effects of ambient ozone on mortality in Europe and North America: results from the APHENA study. Air Qual Atmos Health. 6(2), 445-453(2013).
[19]	Ling Tong, Kai Li, Qixing Zhou. Promoted relationship of cardiovascular morbidity with air pollutants in a typical Chinese urban area. Plos One. 9(9), 1-7(2014).
[20]	Valerie B Haley, Thomas O, Henry D. Surveillance of the short-term impact of fine particle air pollution on cardiovascular disease hospitalizations in New York State. Environmental Health. 42(8), 1-10(2009).
[21]	Richard T, Jeffery R. Effects of particulate and gaseous air pollution on cardiorespiratory hospitalizations. Archives of environmental health. 54(2), 130-139(1999).
[22]	Leah J, Scott L. Are the acute effects of particulate matter on mortality in the national morbidity, mortality, and air pollution study the result of inadequate control for weather and season? A sensitivity analysis using flexible distributed lag models. American Journal of Epidemiology. 162(1), 80-88(2005).
[23]	Douglas W, David Q, Patrick G, et al. Effect of air pollution control on mortality and hospital admissions in Ireland. Health Effects Institute Research Report. 176, 3-109(2013).
[24]	Minzhen Wang, Shan Zheng, Shilin He, et al. The association between diurnal temperature range and emergency room admissions for cardiovascular, respiratory, digestive and genitourinary disease among the elderly: A time series study. Science of the Total Environment. 456(7), 370-375(2013).

Cite This Article

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

Copy | Download

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

Copy | Download

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

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 -

Copy | Download

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

Download PDF

Sections

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

Copy | Download

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

Copy | Download

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