Abstract
BACKGROUND: Men who have sex with men (MSM) are at a high risk for HIV infection. While pre-exposure prophylaxis (PrEP) is an effective oral preventive strategy, its success is largely dependent on consistent medication adherence. OBJECTIVE: The aim of this study was to develop the machine learning web application and evaluate the performance in predicting PrEP adherence. METHODS: The PrEP prospective cohort study of the MSM population conducted in Western China from 2019 to 2023, and we collected adherence data and personal characteristics data from 747 MSM. Predictor variables were screened and the performance of several machine learning methods in predicting nonadherent behaviors were compared. RESULTS: A total of 11 candidate variables were screened that predicted nonadherent behaviors. We developed and evaluated five machine learning models that performed well in predicting adherence. Attitudes of male sexual partners, self-efficacy, HIV testing, number of male sexual partners, and risk perception were the most important predictors of adherence. The optimal prediction model was displayed in a shiny web application for online calculation of the probability of occurrence of nonadherent behaviors among MSM. CONCLUSIONS: Machine learning performed well in predicting nonadherent behaviors among MSM. An interactive and intuitive web application can help identify individuals who may have nonadherent behaviors, resulting in improved medication adherence and increased prevention efficacy.