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 socio-behavioural predictors of HIV for screening.

Method

We used an agglomerative hierarchical, unsupervised machine learning, approach for clustering to analyse data for 146,733 male and 155,622 female respondents from 13 sub-Saharan African countries with 20 and 26 features, respectively, using Population-based HIV Impact Assessment (PHIA) data from the survey years 2015-2019. We employed agglomerative hierarchical clustering and optimal silhouette index criterion to identify clusters of c

SUBMITTER: Mutai CK 

PROVIDER: S-EPMC10354889 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature