Machine learning prediction of adolescent HIV testing services in Ethiopia.

ABSTRACT:

Background

Despite endeavors to achieve the Joint United Nations Programme on HIV/AIDS 95-95-95 fast track targets established in 2014 for HIV prevention, progress has fallen short. Hence, it is imperative to identify factors that can serve as predictors of an adolescent's HIV status. This identification would enable the implementation of targeted screening interventions and the enhancement of healthcare services. Our primary objective was to identify these predictors to facilitate the improvement of HIV testing services for adolescents in Ethiopia.

Methods

A study was conducted by utilizing eight different machine learning techniques to develop models using demographic and health data from 4,502 adolescent respondents. The dataset consisted of 31 variables and variable sele

SUBMITTER: Alie MS 

PROVIDER: S-EPMC10981275 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature