Prediction of hepatitis E using machine learning models.

Journal: PloS one
Published Date: Jan 1, 2020

Abstract

BACKGROUND: Accurate and reliable predictions of infectious disease can be valuable to public health organizations that plan interventions to decrease or prevent disease transmission. A great variety of models have been developed for this task. However, for different data series, the performance of these models varies. Hepatitis E, as an acute liver disease, has been a major public health problem. Which model is more appropriate for predicting the incidence of hepatitis E? In this paper, three different methods are used and the performance of the three methods is compared.

Authors

  • Yanhui Guo
    Department of Computer Science, University of Illinois Springfield, Springfield, IL, United States.
  • Yi Feng
    Department of Urology, Chinese People's Liberation Army General Hospital, Beijing, 100039 China.
  • Fuli Qu
    School of Data and Computer Science, Shandong Women's Unversity, Jinan, Shandong, China.
  • Li Zhang
    Department of Animal Nutrition and Feed Science, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China.
  • Bingyu Yan
    Shandong Provincial Key Laboratory of Infectious Disease Control and Prevention, Shandong Center for Disease Control and Prevention, Jinan, Shandong, China.
  • Jingjing Lv
    Shandong Provincial Key Laboratory of Infectious Disease Control and Prevention, Shandong Center for Disease Control and Prevention, Jinan, Shandong, China.

Keywords

External Resources

Popular Topics
Recent Journals