Predicting place of delivery choice among childbearing women in East Africa: a comparative analysis of advanced machine learning techniques.

Journal: Frontiers in public health
PMID: 39664535

Abstract

BACKGROUND: Sub-Saharan Africa faces high neonatal and maternal mortality rates due to limited access to skilled healthcare during delivery. This study aims to improve the classification of health facilities and home deliveries using advanced machine learning techniques and to explore factors influencing women's choices of delivery locations in East Africa.

Authors

  • Habtamu Setegn Ngusie
    Department of Health Informatics, School of Public Health, College of Medicine and Health Sciences, Woldia University, Woldia, Ethiopia.
  • Getanew Aschalew Tesfa
    School of Public Health, College of Medicine and Health Science, Dilla University, Dilla, Ethiopia.
  • Asefa Adimasu Taddese
    Department of Health Informatics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia.
  • Ermias Bekele Enyew
    Department of Health Informatics, College of Medicine and Health Science, Wollo University, Dessie, Ethiopia.
  • Tilahun Dessie Alene
    Department of Pediatric and Child Health, School of Medicine, College of Medicine and Health Science, Wollo University, Dessie, Ethiopia.
  • Gebremeskel Kibret Abebe
    Department of Emergency and Critical Care Nursing, School of Nursing, College of Medicine and Health Sciences, Woldia University, Woldia, Ethiopia.
  • Agmasie Damtew Walle
    Department of Health Informatics, College of Medicine and Health Science, Debre Berhan University, Debre Berhan, Ethiopia.
  • Alemu Birara Zemariam
    Department of Pediatrics and Child Health Nursing, School of Nursing, College of Medicine and Health Science, Woldia University, Woldia, Ethiopia.

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