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

doi:10.1109/ISPACS48206.2019.8986244

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 proceeding › Conference contribution

10 Citations (Scopus)

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 language	English
Title of host publication	Proceedings - 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728130385
DOIs	https://doi.org/10.1109/ISPACS48206.2019.8986244
Publication status	Published - 2019 Dec
Externally published	Yes
Event	2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019 - Taipei, Taiwan Duration: 2019 Dec 3 → 2019 Dec 6

Publication series

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

Conference

Conference	2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019
Country/Territory	Taiwan
City	Taipei
Period	2019/12/03 → 2019/12/06

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

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

Access to Document

10.1109/ISPACS48206.2019.8986244

Cite this

Jhuo, S. L., Hsieh, M. T., Weng, T. C., Chen, M. J., Yang, C. M., & Yeh, C. H. (2019). Trend Prediction of Influenza and the Associated Pneumonia in Taiwan Using Machine Learning. In Proceedings - 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019 Article 8986244 (Proceedings - 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISPACS48206.2019.8986244

Trend Prediction of Influenza and the Associated Pneumonia in Taiwan Using Machine Learning. / Jhuo, Sing Ling; Hsieh, Mi Tren; Weng, Ting Chien et al.
Proceedings - 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019. Institute of Electrical and Electronics Engineers Inc., 2019. 8986244 (Proceedings - 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Jhuo, SL, Hsieh, MT, Weng, TC, Chen, MJ, Yang, CM & Yeh, CH 2019, Trend Prediction of Influenza and the Associated Pneumonia in Taiwan Using Machine Learning. in Proceedings - 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019., 8986244, Proceedings - 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019, Institute of Electrical and Electronics Engineers Inc., 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019, Taipei, Taiwan, 2019/12/03. https://doi.org/10.1109/ISPACS48206.2019.8986244

Jhuo SL, Hsieh MT, Weng TC, Chen MJ, Yang CM, Yeh CH. Trend Prediction of Influenza and the Associated Pneumonia in Taiwan Using Machine Learning. In Proceedings - 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019. Institute of Electrical and Electronics Engineers Inc. 2019. 8986244. (Proceedings - 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019). doi: 10.1109/ISPACS48206.2019.8986244

Jhuo, Sing Ling ; Hsieh, Mi Tren ; Weng, Ting Chien et al. / Trend Prediction of Influenza and the Associated Pneumonia in Taiwan Using Machine Learning. Proceedings - 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019. Institute of Electrical and Electronics Engineers Inc., 2019. (Proceedings - 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019).

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title = "Trend Prediction of Influenza and the Associated Pneumonia in Taiwan Using Machine Learning",

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.",

keywords = "flu, influenza, machine learning, multilayer perceptron, pneumonia",

author = "Jhuo, \{Sing Ling\} and Hsieh, \{Mi Tren\} and Weng, \{Ting Chien\} and Chen, \{Mei Juan\} and Yang, \{Chieh Ming\} and Yeh, \{Chia Hung\}",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019 ; Conference date: 03-12-2019 Through 06-12-2019",

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AU - Weng, Ting Chien

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AU - Yang, Chieh Ming

AU - Yeh, Chia Hung

PY - 2019/12

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AB - 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.

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KW - influenza

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

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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