Predicting Risky Sexual Behavior Among College Students Through Machine Learning Approaches: Cross-sectional Analysis of Individual Data From 1264 Universities in 31 Provinces in China.

ABSTRACT:

Background

Risky sexual behavior (RSB), the most direct risk factor for sexually transmitted infections (STIs), is common among college students. Thus, identifying relevant risk factors and predicting RSB are important to intervene and prevent RSB among college students.

Objective

We aim to establish a predictive model for RSB among college students to facilitate timely intervention and the prevention of RSB to help limit STI contraction.

Methods

We included a total of 8794 heterosexual Chinese students who self-reported engaging in sexual intercourse from November 2019 to February 2020. We identified RSB among those students and attributed it to 4 dimensions: whether contraception was used, whether the contraceptive method was safe, whether students engaged in casual

SUBMITTER: Li X 

PROVIDER: S-EPMC9909517 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature