Trend Prediction of Influenza and the Associated Pneumonia in Taiwan Using Machine Learning

Sing Ling Jhuo, Mi Tren Hsieh, Ting Chien Weng, Mei Juan Chen, Chieh Ming Yang, Chia Hung Yeh

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

Abstract

Trend prediction of influenza and the associated pneumonia can provide the information for taking preventive actions for public health. This paper uses meteorological and pollution parameters, and acute upper respiratory infection (AVRI) outpatient number as input to multilayer perceptron (MLP) to predict the patient number of influenza and the associated pneumonia in the following week. The meteorological parameters in use are temperature and relative humidity, air pollution parameters are Particulate Matter 2.5 (PM 2.5) and Carbon Monoxide (CO), and the patient prediction includes both outpatients and inpatients. Patients are classified by tertiles into three categories: high, moderate, and low volumes. In the nationwide data analysis, the proposed method using MLP machine learning can reach the accuracy of 81.16% for the elderly population and 77.54% for overall population in Taiwan. The regional data analyses with various age groups are also provided in this paper.

Original languageEnglish
Title of host publicationProceedings - 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728130385
DOIs
Publication statusPublished - 2019 Dec
Externally publishedYes
Event2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019 - Taipei, Taiwan
Duration: 2019 Dec 32019 Dec 6

Publication series

NameProceedings - 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019

Conference

Conference2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019
Country/TerritoryTaiwan
CityTaipei
Period2019/12/032019/12/06

Keywords

  • flu
  • influenza
  • machine learning
  • multilayer perceptron
  • pneumonia

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Computer Networks and Communications

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