Causal machine learning models for predicting low birth weight in midwife-led continuity care intervention in North Shoa Zone, Ethiopia
Based on these findings, the CKNN classifier demonstrated a higher accuracy and effectiveness. The S-learner and R-learner models, utilizing the XGBoost Regressor and BaseSRegressor, provided accurate estimations of ITE for assessing the impact of the MLCC program. Promoting the MLCC program could help stabilize LBW outcomes.
Authors
- Wudneh Ketema Moges
- Aweke A. Mitku (ORCID: https://orcid.org/0000-0002-6129-9558)
- Solomon Hailemeskel (ORCID: https://orcid.org/0000-0002-8441-5558)
- Esubalew Tesfahun (ORCID: https://orcid.org/0000-0002-1848-1872)
- Awoke Seyoum Tegegne (ORCID: https://orcid.org/0000-0001-9132-8025)
Institutions
- Debre Berhan University (ET)
- University of the Witwatersrand (ZA)
- Bahir Dar University (ET)
Publication Details
- Journal
- BMC Medical Informatics and Decision Making
- Published
- 2025-02-07
- DOI
- https://doi.org/10.1186/s12911-025-02917-9
- Citations
- 6
- Primary Topic
- Global Maternal and Child Health
- Type
- article
- Field-Weighted Citation Impact
- 20.96
Funders
- Bahir Dar University