Prediction of Outpatient Visits for Upper Respiratory Tract Infections by Machine Learning of PM2.5 and PM10 Levels in Taiwan

Pei Hsuan Yang, Mi Tren Hsieh, Gen Min Lin, Mei Juan Chen, Chia Hung Yeh, Zhi Xiang Huang, Chieh Ming Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Particulate Matter (PM) 2.5 and PM10 are referred as a mixture of liquid droplets and solid particles in the air with diameters leq 2.5 mum and leq 10 mum, respectively. Both PM2.5 and PM10 can deposit on respiratory tract and trigger inflammatory reactions, which makes the respiratory tract predisposed to infections. The study used machine learning on daily PM2.5 and PM10 levels of consecutive 30 days from the open website datasets of Environment Protection Administration between Dec. 2008 and Dec. 2016 to predict the subsequent one-week outpatient visits for upper respiratory tract infections (URI) from the Centers for Disease Control (CDC) in Taiwan between Jan. 2009 and Dec. 2016. The weekly URI cases were classified by tertile as high, moderate, and low volumes. In general, both URI burden and PM levels peak in winter and spring seasons. The testing used the mid-month dataset of each season (Jan., Apr., Jul., and Oct.), and the training used the other months datasets. In the nationwide data analysis, PM2.5 and PM10 levels input to the multilayer perceptron (MLP) can precisely predict the degree of URI number for the elderly (89.05% and 88.32%, respectively) and the overall population (81.75% and 83.21%, respectively). In conclusion, machine learning of PM2.5 and PM10 levels could accurately predict the burden of outpatient visits for URI in Taiwan.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781538663011
DOIs
Publication statusPublished - 2018 Aug 27
Event5th IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2018 - Taichung, Taiwan
Duration: 2018 May 192018 May 21

Publication series

Name2018 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2018

Conference

Conference5th IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2018
Country/TerritoryTaiwan
CityTaichung
Period2018/05/192018/05/21

Keywords

  • air pollution
  • machine learning
  • PM10
  • PM2.5
  • upper respiratory infections

ASJC Scopus subject areas

  • Biomedical Engineering
  • Electrical and Electronic Engineering
  • Instrumentation

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