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- Title
Predicting Adolescent Intervention Non-responsiveness for Precision HIV Prevention Using Machine Learning.
- Authors
Wang, Bo; Liu, Feifan; Deveaux, Lynette; Ash, Arlene; Gerber, Ben; Allison, Jeroan; Herbert, Carly; Poitier, Maxwell; MacDonell, Karen; Li, Xiaoming; Stanton, Bonita
- 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 random forest model using longitudinal data and the Boruta feature selection approach predicted intervention non-responsiveness best, achieving sensitivity of 85.4%, specificity of 78.4% and AUROC of 0.93 on the training data, and sensitivity of 84.3%, specificity of 67.1%, and AUROC of 0.85 on the test data. Key predictors include self-efficacy, perceived response cost, parent monitoring, vulnerability, response efficacy, HIV/AIDS knowledge, communication about condom use, and severity of HIV/STI. Machine learning can yield powerful predictive models to identify adolescents who are unlikely to respond to an intervention. Such models can guide the development of alternative strategies that may be more effective with intervention non-responders.
- Subjects
BAHAMAS; HIV prevention; HIV infection risk factors; RISK-taking behavior; HEALTH education; SUPPORT vector machines; DECISION trees; PSYCHOLOGICAL vulnerability; INDIVIDUALIZED medicine; MACHINE learning; RANDOM forest algorithms; PREVENTIVE health services; RISK assessment; SELF-efficacy; HEALTH literacy; SEVERITY of illness index; TEENAGERS' conduct of life; HEALTH behavior; PSYCHOLOGY of high school students; RESEARCH funding; DESCRIPTIVE statistics; COMMUNICATION; PREDICTION models; LOGISTIC regression analysis; SENSITIVITY &; specificity (Statistics); RECEIVER operating characteristic curves; PARENT-child relationships; CONDOMS; ALGORITHMS; LONGITUDINAL method; UNSAFE sex; ADOLESCENCE
- Publication
AIDS & Behavior, 2023, Vol 27, Issue 5, p1392
- ISSN
1090-7165
- Publication type
Article
- DOI
10.1007/s10461-022-03874-4