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Optimized Neural Network Based on Genetic Algorithm to Construct Hand-Foot-and-Mouth Disease Prediction and Early-Warning Model

Author

Listed:
  • Xialv Lin

    (School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China
    These authors contributed equally to this work.)

  • Xiaofeng Wang

    (Chinese Center for Disease Control and Prevention, Beijing 102206, China
    These authors contributed equally to this work.)

  • Yuhan Wang

    (School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China
    These authors contributed equally to this work.)

  • Xuejie Du

    (Chinese Center for Disease Control and Prevention, Beijing 102206, China)

  • Lizhu Jin

    (Chinese Center for Disease Control and Prevention, Beijing 102206, China)

  • Ming Wan

    (Chinese Center for Disease Control and Prevention, Beijing 102206, China
    These authors contributed equally to this work.)

  • Hui Ge

    (Chinese Center for Disease Control and Prevention, Beijing 102206, China
    These authors contributed equally to this work.)

  • Xu Yang

    (School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China
    These authors contributed equally to this work.)

Abstract

Accompanied by the rapid economic and social development, there is a phenomenon of the crazy spread of many infectious diseases. It has brought the rapid growth of the number of people infected with hand-foot-and-mouth disease (HFMD), and children, especially infants and young children’s health is at great risk. So it is very important to predict the number of HFMD infections and realize the regional early-warning of HFMD based on big data. However, in the current field of infectious diseases, the research on the prevalence of HFMD mainly predicts the number of future cases based on the number of historical cases in various places, and the influence of many related factors that affect the prevalence of HFMD is ignored. The current early-warning research of HFMD mainly uses direct case report, which uses statistical methods in time and space to have early-warnings of outbreaks separately. It leads to a high error rate and low confidence in the early-warning results. This paper uses machine learning methods to establish a HFMD epidemic prediction model and explore constructing a variety of early-warning models. By comparison of experimental results, we finally verify that the HFMD prediction algorithm proposed in this paper has higher accuracy. At the same time, the early-warning algorithm based on the comparison of threshold has good results.

Suggested Citation

  • Xialv Lin & Xiaofeng Wang & Yuhan Wang & Xuejie Du & Lizhu Jin & Ming Wan & Hui Ge & Xu Yang, 2021. "Optimized Neural Network Based on Genetic Algorithm to Construct Hand-Foot-and-Mouth Disease Prediction and Early-Warning Model," IJERPH, MDPI, vol. 18(6), pages 1-25, March.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:6:p:2959-:d:516616
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    References listed on IDEAS

    as
    1. Xu Yang & Guo Chen & Yunchong Qian & Yuhan Wang & Yisong Zhai & Debao Fan & Yang Xu, 2020. "Prediction of Myopia in Adolescents through Machine Learning Methods," IJERPH, MDPI, vol. 17(2), pages 1-14, January.
    2. Honglong Zhang & Liping Wang & Shengjie Lai & Zhongjie Li & Qiao Sun & Peng Zhang, 2017. "Surveillance and early warning systems of infectious disease in China: From 2012 to 2014," International Journal of Health Planning and Management, Wiley Blackwell, vol. 32(3), pages 329-338, July.
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    Cited by:

    1. Zhiyuan Hao & Jie Ma & Wenjing Sun, 2022. "The Technology-Oriented Pathway for Auxiliary Diagnosis in the Digital Health Age: A Self-Adaptive Disease Prediction Model," IJERPH, MDPI, vol. 19(19), pages 1-23, September.
    2. Tim Hulsen, 2022. "Data Science in Healthcare: COVID-19 and Beyond," IJERPH, MDPI, vol. 19(6), pages 1-4, March.

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