Use of unsupervised machine learning to characterise HIV predictors in sub-Saharan Africa

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 socio-behav...

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Detalles Bibliográficos
Autores principales: Mutai, Charles K., McSharry, Patrick E., Ngaruye, Innocent, Musabanganji, Edouard
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10354889/
https://www.ncbi.nlm.nih.gov/pubmed/37468851
http://dx.doi.org/10.1186/s12879-023-08467-7

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10354889/
https://www.ncbi.nlm.nih.gov/pubmed/37468851
http://dx.doi.org/10.1186/s12879-023-08467-7

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