Abstract
OBJECTIVE: This study aimed to construct a model based on machine learning to predict new HIV infections in HIV-negative men who have sex with men (MSM). METHODS: This is a secondary analysis of a previous random clinical trial aiming to evaluate the preventive effects of PrEP on new HIV infection in MSM. During 2013-2015, 1455 HIV-negative MSM were enrolled. Participants were divided into treatment group and control group and regularly followed up until they seroconverted to HIV positive or until the 2-year endpoint reached. Five machine-learning approaches were applied to predict the risk of HIV infection. Model performance was evaluated using Harrel's C-index and area under the receiver operator characteristic curve (AUC) and validated in an external validation cohort. To explain this model, shapley additive explanation (SHAP) values were calculated and visualized. RESULTS: During the observation period, 102 MSM developed HIV infection. Thirteen parameters are selected to construct the model. The random survival forest model showed the best performance in the validation cohort, with a C-index of 0.7013, and could significantly categorize MSM into three groups. Our model indicated that MSM with younger age, receptive anal intercourse, and multiple male sexual partners had an increased risk of HIV infection, and those with higher AIDS knowledge scores had a lower risk. CONCLUSION: We presented a machine learning-based model to predict their risk of developing HIV infection. This model could be applied to recognize MSM who are at a higher risk of developing HIV infection.