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. 2018 Aug 16;6(8):200-206.
doi: 10.12998/wjcc.v6.i8.200.

Machine learning to relate PM2.5 and PM10 concentrations to outpatient visits for upper respiratory tract infections in Taiwan: A nationwide analysis

Mei-Juan Chen  1 , Pei-Hsuan Yang  1 , Mi-Tren Hsieh  1 , Chia-Hung Yeh  2 , Chih-Hsiang Huang  3 , Chieh-Ming Yang  1 , Gen-Min Lin  1
Affiliations

Machine learning to relate PM2.5 and PM10 concentrations to outpatient visits for upper respiratory tract infections in Taiwan: A nationwide analysis

Mei-Juan Chen et al. World J Clin Cases. .

Abstract

Aim: To examine the accuracy of machine learning to relate particulate matter (PM) 2.5 and PM10 concentrations to upper respiratory tract infections (URIs).

Methods: Daily nationwide and regional outdoor PM2.5 and PM10 concentrations collected over 30 consecutive days obtained from the Taiwan Environment Protection Administration were the inputs for machine learning, using multilayer perceptron (MLP), to relate to the subsequent one-week outpatient visits for URIs. The URI data were obtained from the Centers for Disease Control datasets in Taiwan between 2009 and 2016. The testing used the middle month dataset of each season (January, April, July and October), and the training used the other months' datasets. The weekly URI cases were classified by tertile as high, moderate, and low volumes.

Results: Both PM concentrations and URI cases peak in winter and spring. In the nationwide data analysis, MLP machine learning can accurately relate the URI volumes of the elderly (89.05% and 88.32%, respectively) and the overall population (81.75% and 83.21%, respectively) with the PM2.5 and PM10 concentrations. In the regional data analyses, greater accuracy is found for PM2.5 than for PM10 for the elderly, particularly in the Central region (78.10% and 74.45%, respectively), whereas greater accuracy is found for PM10 than for PM2.5 for the overall population, particularly in the Northern region (73.19% and 63.04%, respectively).

Conclusion: Short-term PM2.5 and PM10 concentrations were accurately related to the subsequent occurrence of URIs by using machine learning. Our findings suggested that the effects of PM2.5 and PM10 on URI may differ by age, and the mechanism needs further evaluation.

Keywords: Air pollution; Machine learning; Particulate matter 10; Particulate matter 2.5; Upper respiratory infections.

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Conflict of interest statement

Conflict-of-interest statement: The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
Multilayer perceptron model for the proposed algorithm.
Figure 2
Figure 2
The average daily concentrations and weekly numbers of outpatient visits for upper respiratory tract infections in each month. A and B: PM2.5 and PM10, repectively (December 2008 - December 2016); C and D: The overall and the elderly patients, repsectively (January 2009 - December 2016). PM: Particulate matter.
See this image and copyright information in PMC

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