Title

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

UMMS Affiliation

Department of Population and Quantitative Health Sciences

Publication Date

2021-05-01

Document Type

Article

Disciplines

Artificial Intelligence and Robotics | Behavior and Behavior Mechanisms | Epidemiology | Virus Diseases

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.

Keywords

adolescent HIV risk behaviour, machine learning, multiple sex partners, prediction

DOI of Published Version

10.1097/QAD.0000000000002867

Source

Wang B, Liu F, Deveaux L, Ash A, Gosh S, Li X, Rundensteiner E, Cottrell L, Adderley R, Stanton B. Adolescent HIV-related behavioural prediction using machine learning: a foundation for precision HIV prevention. AIDS. 2021 May 1;35(Suppl 1):S75-S84. doi: 10.1097/QAD.0000000000002867. PMID: 33867490; PMCID: PMC8133351. Link to article on publisher's site

Journal/Book/Conference Title

AIDS (London, England)

PubMed ID

33867490

Related Resources

Link to Article in PubMed

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