Use of unsupervised machine learning to characterise HIV predictors in sub-Saharan Africa
Abstract Introduction Significant regional variations in the HIV epidemic hurt effective common interventions in sub-Saharan Africa. It is crucial to analyze HIV positivity distributions within clusters and assess the homogeneity of countries. We aim at identifying clusters of countries based on soc...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2023-07-01
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| Series: | BMC Infectious Diseases |
| Online Access: | https://doi.org/10.1186/s12879-023-08467-7 |