Use of explainable machine learning in risk classification of Cesarean section delivery: A cross-sectional analysis of Demographic and Health Surveys from ten Sub-Saharan African countries (2016-2024).
- DOI
- 10.1371/journal.pgph.0006613
- Published
- 2026
- Container
- PLOS global public health
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1371/journal.pgph.0006613,
title = {Use of explainable machine learning in risk classification of Cesarean section delivery: A cross-sectional analysis of Demographic and Health Surveys from ten Sub-Saharan African countries (2016-2024).},
author = {Birhanu WA and Tilahun B and Yehuala TZ and Maru TC and Ayalaw TB and Baykemagn ND and Gedefaw AE},
year = {2026},
journal = {PLOS global public health},
doi = {10.1371/journal.pgph.0006613},
url = {https://doi.org/10.1371/journal.pgph.0006613}
}RIS
TY - JOUR TI - Use of explainable machine learning in risk classification of Cesarean section delivery: A cross-sectional analysis of Demographic and Health Surveys from ten Sub-Saharan African countries (2016-2024). AU - Birhanu WA AU - Tilahun B AU - Yehuala TZ AU - Maru TC AU - Ayalaw TB AU - Baykemagn ND AU - Gedefaw AE PY - 2026 JO - PLOS global public health DO - 10.1371/journal.pgph.0006613 UR - https://doi.org/10.1371/journal.pgph.0006613 ER -
APA
WA, B., B, T., TZ, Y., TC, M., TB, A., ND, B., & AE, G. (2026). Use of explainable machine learning in risk classification of Cesarean section delivery: A cross-sectional analysis of Demographic and Health Surveys from ten Sub-Saharan African countries (2016-2024).. PLOS global public health. https://doi.org/10.1371/journal.pgph.0006613
Source records
- pubmed · retrieved 2026-09-25T18:50:45.704Z