Causal machine learning models for predicting low birth weight in midwife-led continuity care intervention in North Shoa Zone, Ethiopia.

Journal: BMC medical informatics and decision making
PMID: 39920662

Abstract

BACKGROUND: Low birth weight (LBW) is a critical global health issue that affects infants disproportionately, particularly in developing countries. This study adopted causal machine learning (CML) algorithms for predicting LBW in newborns, drawing from midwife-led continuity care (MLCC).

Authors

  • Wudneh Ketema Moges
    Department of Statistics, College of Science, Bahir Dar University, P.O.Box 79, Bahir Dar, Ethiopia. wudnehketema@gmail.com.
  • Awoke Seyoum Tegegne
    College of Science, Bahir Dar University, Bahir Dar, Ethiopia.
  • Aweke A Mitku
    Department of Statistics, College of Science, Bahir Dar University, P.O.Box 79, Bahir Dar, Ethiopia.
  • Esubalew Tesfahun
    Department of Public Health, College of Health Science, Debre Berhan University, P.O.Box 445, Debre Berhan, Ethiopia.
  • Solomon Hailemeskel
    Department of Midwifery, College of Health Science, Debre Berhan University, P.O.Box 445, Debre Berhan, Ethiopia.

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