Machine learning-based risk factor analysis and prevalence prediction of intestinal parasitic infections using epidemiological survey data.

Journal: PLoS neglected tropical diseases
PMID: 35700192

Abstract

BACKGROUND: Previous epidemiological studies have examined the prevalence and risk factors for a variety of parasitic illnesses, including protozoan and soil-transmitted helminth (STH, e.g., hookworms and roundworms) infections. Despite advancements in machine learning for data analysis, the majority of these studies use traditional logistic regression to identify significant risk factors.

Authors

  • Aziz Zafar
    Colgate University, Department of Mathematics, Hamilton, New York, United States of America.
  • Ziad Attia
    Colgate University, Department of Mathematics, Hamilton, New York, United States of America.
  • Mehret Tesfaye
    Addis Ababa University, College of Health Sciences, Department of Medical Laboratory Science, Addis Ababa, Ethiopia.
  • Sosina Walelign
    Addis Ababa University, College of Health Sciences, Department of Medical Laboratory Science, Addis Ababa, Ethiopia.
  • Moges Wordofa
    Addis Ababa University, College of Health Sciences, Department of Medical Laboratory Science, Addis Ababa, Ethiopia.
  • Dessie Abera
    Addis Ababa University, College of Health Sciences, Department of Medical Laboratory Science, Addis Ababa, Ethiopia.
  • Kassu Desta
    Addis Ababa University, College of Health Sciences, Department of Medical Laboratory Science, Addis Ababa, Ethiopia.
  • Aster Tsegaye
    Addis Ababa University, College of Health Sciences, Department of Medical Laboratory Science, Addis Ababa, Ethiopia.
  • Ahmet Ay
    Department of Biology, Colgate University, Hamilton, NY, United States.
  • Bineyam Taye
    Department of Biology, Colgate University, Hamilton, NY, United States.

Keywords

External Resources

Popular Topics
Recent Journals