Application of the random forest algorithm to predict skilled birth attendance and identify determinants among reproductive-age women in 27 Sub-Saharan African countries; machine learning analysis
The findings highlight the potential of machine learning to identify critical predictors of skilled birth attendance to inform targeted interventions. Addressing socioeconomic and educational disparities, enhancing healthcare access, and implementing tailored cessation programs are crucial to enhance skilled birth attendance in this vulnerable population.
Authors
- Abriham Zegeye
- Tarekegn Cheklie Zeleke (ORCID: https://orcid.org/0000-0002-6120-3552)
- Abel Temeche Kassaw (ORCID: https://orcid.org/0009-0008-3431-8187)
- Fetlework Gubena Arage
- Eliyas Addisu Taye (ORCID: https://orcid.org/0009-0005-1561-072X)
- Eden Yitbarek Woubet
- Gabrela Yimer Hailie
- Tigabu Eskeziya Zerihun (ORCID: https://orcid.org/0009-0005-2616-397X)
Institutions
- Debre Tabor University (ET)
- University of Gondar (ET)
Publication Details
- Journal
- BMC Public Health
- Published
- 2025-03-06
- DOI
- https://doi.org/10.1186/s12889-025-22007-9
- Citations
- 12
- Primary Topic
- Global Maternal and Child Health
- Type
- article
- Field-Weighted Citation Impact
- 41.93