Predicting Adolescent Intervention Non-responsiveness for Precision HIV Prevention Using Machine Learning.

ABSTRACT: Interventions to teach protective behaviors may be differentially effective within an adolescent population. Identifying the characteristics of youth who are less likely to respond to an intervention can guide program modifications to improve its effectiveness. Using comprehensive longitudinal data on adolescent risk behaviors, perceptions, sensation-seeking, peer and family influence, and neighborhood risk factors from 2564 grade 10-12 students in The Bahamas, this study employs machine learning approaches (support vector machines, logistic regression, decision tree, and random forest) to identify important predictors of non-responsiveness for precision prevention. We used 80% of the data to train the models and the rest for model testing. Among different machine learning algorithms, the

SUBMITTER: Wang B 

PROVIDER: S-EPMC10129965 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature