Prediction of caesarean section birth using machine learning algorithms among pregnant women in a district hospital in Ghana.

Journal: BMC pregnancy and childbirth
Published Date: Jul 2, 2025

Abstract

BACKGROUND: Machine learning algorithms may contribute to improving maternal and child health, including determining the suitability of caesarean section (CS) births in low-resource countries. Despite machine learning algorithms offering a more robust approach to predicting/diagnosing a health-related problem, research on their use in determining CS birth is scarce in sub-Saharan Africa. This study therefore aimed to compare the performance of five machine learning techniques in predicting CS birth among pregnant women in a district hospital in Ghana.

Authors

  • Frederick Osei Owusu
    Juaben Government Hospital, Juaben, Ghana.
  • Helena Addai-Manu
    Juaben Government Hospital, Juaben, Ghana.
  • Esther Serwah Agbedinu
    Juaben Government Hospital, Juaben, Ghana.
  • Emmanuel Konadu
    Department of Epidemiology and Biostatistics, School of Public Health, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana.
  • Lydia Asenso
    Juaben Government Hospital, Juaben, Ghana.
  • Mercy Addae
    Department of Epidemiology and Biostatistics, School of Public Health, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana.
  • Joseph Osarfo
    Department of Community Health, School of Medicine, University of Health and Allied Health Sciences, Ho, Ghana.
  • Brenda Abena Ampah
    University Hospital, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana.
  • Douglas Aninng Opoku
    Department of Epidemiology and Biostatistics, School of Public Health, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana. douglasopokuaninng@gmail.com.

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