Prediction of stunting and its socioeconomic determinants among adolescent girls in Ethiopia using machine learning algorithms.

Journal: PloS one
PMID: 39854425

Abstract

BACKGROUND: Stunting is a vital indicator of chronic undernutrition that reveals a failure to reach linear growth. Investigating growth and nutrition status during adolescence, in addition to infancy and childhood is very crucial. However, the available studies in Ethiopia have been usually focused in early childhood and they used the traditional stastical methods. Therefore, this study aimed to employ multiple machine learning algorithms to identify the most effective model for the prediction of stunting among adolescent girls in Ethiopia.

Authors

  • Alemu Birara Zemariam
    Department of Pediatrics and Child Health Nursing, School of Nursing, College of Medicine and Health Science, Woldia University, Woldia, Ethiopia.
  • Biruk Beletew Abate
    College of Health Science, Woldia University, Woldia, Ethiopia.
  • Addis Wondmagegn Alamaw
    Department of Emergency and Critical Care Nursing, School of Nursing, College of Medicine and Health Science, Woldia University, Woldia, Ethiopia.
  • Eyob Shitie Lake
    Department of Midwifery, School of Midwifery, School of Midwifery, College of Medicine and Health Science, Woldia University, Woldia, Ethiopia.
  • Gizachew Yilak
    Department of Nursing, School of Nursing, College of Medicine and Health Science, Woldia University, Woldia, Ethiopia.
  • Mulat Ayele
    Department of Midwifery, School of Midwifery, School of Midwifery, College of Medicine and Health Science, Woldia University, Woldia, Ethiopia.
  • Befkad Derese Tilahun
    Department of Nursing, School of Nursing, College of Medicine and Health Science, Woldia University, Woldia, Ethiopia.
  • Habtamu Setegn Ngusie
    Department of Health Informatics, School of Public Health, College of Medicine and Health Sciences, Woldia University, Woldia, Ethiopia.

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