Predicting the Risk of HIV Infection and Sexually Transmitted Diseases Among Men Who Have Sex With Men: Cross-Sectional Study Using Multiple Machine Learning Approaches.

ABSTRACT:

Background

Men who have sex with men (MSM) are at high risk for HIV infection and sexually transmitted diseases (STDs). However, there is a lack of accurate and convenient tools to assess this risk.

Objective

This study aimed to develop machine learning models and tools to predict and assess the risk of HIV infection and STDs among MSM.

Methods

We conducted a cross-sectional study that collected individual characteristics of 1999 MSM with negative or unknown HIV serostatus in Western China from 2013 to 2023. MSM self-reported their STD history and were tested for HIV. We compared the accuracy of 6 machine learning methods in predicting the risk of HIV infection and STDs using 7 parameters for a comprehensive assessment, ranking the methods according to their performan

SUBMITTER: Lin B 

PROVIDER: S-EPMC11888048 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature