Adolescent HIV-related behavioural prediction using machine learning: a foundation for precision HIV prevention.

ABSTRACT:

Background

Precision prevention is increasingly important in HIV prevention research to move beyond universal interventions to those tailored for high-risk individuals. The current study was designed to develop machine learning algorithms for predicting adolescent HIV risk behaviours.

Methods

Comprehensive longitudinal data on adolescent risk behaviours, perceptions, peer and family influence, and neighbourhood risk factors were collected from 2564 grade-10 students at baseline followed for 24 months over 2008-2012. Machine learning techniques [support vector machine (SVM) and random forests] were applied to innovatively leverage longitudinal data for robust HIV risk behaviour prediction. In this study, we focused on two adolescent risk behaviours: had ever had sex and had m

SUBMITTER: Wang B 

PROVIDER: S-EPMC8133351 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature