Adolescent HIV-related behavioural prediction using machine learning: a foundation for precision HIV prevention. (1st May 2021)

• Record Type: Journal Article
• Authors: Wang, Bo; Liu, Feifan; Deveaux, Lynette; Ash, Arlene; Gosh, Samiran; Li, Xiaoming; Rundensteiner, Elke; Cottrell, Lesley; Adderley, Richard; Stanton, Bonita
• Is Part Of: AIDS. Volume 35:Supplement 1(2021)
• Publication Date: 2021-05-01
• Subjects: adolescent HIV risk behaviour -- machine learning -- multiple sex partners -- prediction; AIDS (Disease) -- Periodicals; Acquired Immunodeficiency Syndrome; AIDS (Disease); Periodicals; Periodicals; 616.9792005
• DOI: 10.1097/QAD.0000000000002867
• Languages: English
• ISSNs: 0269-9370

Abstract

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 multiple sex partners. Twenty percent of the data were withheld for model testing. Results: The SVM model with cost-sensitive learning achieved the highest sensitivity, at 79.1%, specificity of 75.4% with AUC of 0.86 in predicting multiple sex partners on the training data (10-fold cross-validation), and sensitivity of 79.7%, specificity of 76.5% with AUC of 0.86 on the testing data. The random forest model obtained the best performance in predicting had ever had sex, yielding the sensitivity of 78.5%, specificity of 73.1% with AUC of 0.84 on the training data and sensitivity of 82.7%, specificity of 75.3% with AUC of 0.87 on the testing data. Conclusion: Machine learning methods can be used to build effective prediction model(s) to identify adolescents who are likely to engage in HIV risk behaviours. This study builds a foundation for targeted intervention strategies and informs precision prevention efforts in school-setting. Abstract : Supplemental Digital Content is available in the text