Machine learning approach and geospatial analysis to determine HIV infection, awareness status, and transmission knowledge among adults in Sub-Saharan Africa
- DOI
- 10.1186/s13104-024-07053-7
- Published
- 2024-12-23
- Container
- BMC Research Notes
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1186/s13104-024-07053-7,
title = {Machine learning approach and geospatial analysis to determine HIV infection, awareness status, and transmission knowledge among adults in Sub-Saharan Africa},
author = {Abel Endawkie and Birhan Asmame Miheretu and Anteneh Yalew and Peter S. Nyasulu and Getahun Worku and Ashebir Asaminew and Bayuh Asmamaw Hailu},
year = {2024},
journal = {BMC Research Notes},
doi = {10.1186/s13104-024-07053-7},
url = {https://doi.org/10.1186/s13104-024-07053-7}
}RIS
TY - JOUR TI - Machine learning approach and geospatial analysis to determine HIV infection, awareness status, and transmission knowledge among adults in Sub-Saharan Africa AU - Abel Endawkie AU - Birhan Asmame Miheretu AU - Anteneh Yalew AU - Peter S. Nyasulu AU - Getahun Worku AU - Ashebir Asaminew AU - Bayuh Asmamaw Hailu PY - 2024 JO - BMC Research Notes DO - 10.1186/s13104-024-07053-7 UR - https://doi.org/10.1186/s13104-024-07053-7 ER -
APA
Endawkie, A., Miheretu, B. A., Yalew, A., Nyasulu, P. S., Worku, G., Asaminew, A., & Hailu, B. A. (2024). Machine learning approach and geospatial analysis to determine HIV infection, awareness status, and transmission knowledge among adults in Sub-Saharan Africa. BMC Research Notes. https://doi.org/10.1186/s13104-024-07053-7
Source records
- crossref · retrieved 2026-09-27T12:25:20.398Z