A supervised machine learning statistical design of experiment approach to modeling the barriers to effective snakebite treatment in Ghana.

Journal: PLoS neglected tropical diseases
PMID: 39671447

Abstract

BACKGROUND: Snakebite envenoming is a serious condition that affects 2.5 million people and causes 81,000-138,000 deaths every year, particularly in tropical and subtropical regions. The World Health Organization has set a goal to halve the deaths and disabilities related to snakebite envenoming by 2030. However, significant challenges in achieving this goal include a lack of robust research evidence related to snakebite incidence and treatment, particularly in sub-Saharan Africa. This study aimed to combine established methodologies with the latest tools in Artificial Intelligence to assess the barriers to effective snakebite treatment in Ghana.

Authors

  • Eric Nyarko
    Department of Statistics and Actuarial Science, College of Basic and Applied Sciences, University of Ghana, Legon, Accra, Ghana.
  • Edmund Fosu Agyemang
    Department of Statistics and Actuarial Science, College of Basic and Applied Sciences, University of Ghana, Legon, Accra, Ghana.
  • Ebenezer Kwesi Ameho
    Department of Statistics and Actuarial Science, College of Basic and Applied Sciences, University of Ghana, Legon, Accra, Ghana.
  • Louis Agyekum
    Department of Statistics and Actuarial Science, College of Basic and Applied Sciences, University of Ghana, Legon, Accra, Ghana.
  • José María Gutiérrez
    Instituto Clodomiro Picado, Facultad de Microbiología, Universidad de Costa Rica, San José, Costa Rica.
  • Eduardo Alberto Fernandez
    Department of Health Sciences, Brock University, St Catharines, Ontario, Canada.

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