Predicting zero-dose vaccination status in 27 sub-Saharan African countries: a machine learning approach

Berhanu Fikadie Endehabtu, Eliyas Addisu Taye

Open source

DOI: 10.1136/bmjhci-2026-102188
Published: 2026-09
Container: BMJ Health & Care Informatics
Publisher: BMJ
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1136/bmjhci-2026-102188,
  title = {Predicting zero-dose vaccination status in 27 sub-Saharan African countries: a machine learning approach},
  author = {Berhanu Fikadie Endehabtu and Eliyas Addisu Taye},
  year = {2026},
  journal = {BMJ Health \&amp; Care Informatics},
  doi = {10.1136/bmjhci-2026-102188},
  url = {https://doi.org/10.1136/bmjhci-2026-102188}
}

RIS

TY  - JOUR
TI  - Predicting zero-dose vaccination status in 27 sub-Saharan African countries: a machine learning approach
AU  - Berhanu Fikadie Endehabtu
AU  - Eliyas Addisu Taye
PY  - 2026
JO  - BMJ Health &amp; Care Informatics
DO  - 10.1136/bmjhci-2026-102188
UR  - https://doi.org/10.1136/bmjhci-2026-102188
ER  -

APA

Endehabtu, B. F., & Taye, E. A. (2026). Predicting zero-dose vaccination status in 27 sub-Saharan African countries: a machine learning approach. BMJ Health & Care Informatics. https://doi.org/10.1136/bmjhci-2026-102188

Source records

crossref · retrieved 2026-09-26T14:15:52.566Z