Prediction of delayed breastfeeding initiation among mothers having children less than 2 months of age in East Africa: application of machine learning algorithms.

Journal: Frontiers in public health
PMID: 39286748

Abstract

BACKGROUND: Delayed breastfeeding initiation is a significant public health concern, and reducing the proportion of delayed breastfeeding initiation in East Africa is a key strategy for lowering the Child Mortality rate. However, there is limited evidence on this public health issue assessed using advanced models. Therefore, this study aimed to assess prediction of delayed initiation of breastfeeding initiation and associated factors among women with less than 2 months of a child in East Africa using the machine learning approach.

Authors

  • Agmasie Damtew Walle
    Department of Health Informatics, College of Medicine and Health Science, Debre Berhan University, Debre Berhan, Ethiopia.
  • Zenebe Abebe Gebreegziabher
    Department of Epidemiology and Biostatistics, School of Public Health, Debre Berhan University, Debre Birhan, Ethiopia.
  • Habtamu Setegn Ngusie
    Department of Health Informatics, School of Public Health, College of Medicine and Health Sciences, Woldia University, Woldia, Ethiopia.
  • Sisay Yitayih Kassie
    Department of Health Informatics, School of Public Health, College of Medicine and Health Science, Hawassa University, Hawassa, Ethiopia.
  • Abera Lambebo
    Department of Public Health, School of Public Health, Debre Berhan University, Debre Birhan, Ethiopia.
  • Fitsum Zekarias
    Department of Public Health, School of Public Health, Debre Berhan University, Debre Birhan, Ethiopia.
  • Tadesse Mamo Dejene
    Department of Public Health, School of Public Health, Debre Berhan University, Debre Birhan, Ethiopia.
  • Shimels Derso Kebede
    Department of Health Informatics, School of Public Health, College of Medicine and Health Sciences, Wollo University, Dessie, Ethiopia.

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