Exploring machine learning algorithms to predict short birth intervals and identify its determinants among reproductive-age women in East Africa.

Journal: BMC pregnancy and childbirth
PMID: 40346482

Abstract

BACKGROUND: The occurrence of short birth intervals among reproductive-age women in East Africa is a critical public health issue, contributing to maternal and child health risks. Identifying the key factors that predict short birth intervals can help design targeted interventions to reduce these risks. Hence, this study aimed to predict short birth intervals and identify their determinants using supervised machine learning models.

Authors

  • Tirualem Zeleke Yehuala
    Department Health informatics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia. sarazeleke3@gmail.com.
  • Bezawit Melak Fente
    Department of General Midwifery, School of Midwifery, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia.
  • Sisay Maru Wubante
    Department Health informatics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia.

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