Application of machine learning for risky sexual behavior interventions among factory workers in China

Fang Zhang; Shiben Zhu; Siyu Chen; Ziyu Hao; Yuan Fang; Huachun Zou; Yong Cai; Bolin Cao; Kechun Zhang; He Cao; Yaqi Chen; Tian Hu; Zixin Wang

doi:10.3389/fpubh.2023.1092018

Application of machine learning for risky sexual behavior interventions among factory workers in China

Front Public Health. 2023 Aug 3:11:1092018. doi: 10.3389/fpubh.2023.1092018. eCollection 2023.

Authors

Fang Zhang^#¹, Shiben Zhu^#², Siyu Chen^#², Ziyu Hao^#², Yuan Fang³, Huachun Zou^{4

5}, Yong Cai⁶, Bolin Cao⁷, Kechun Zhang⁸, He Cao⁸, Yaqi Chen⁸, Tian Hu⁸, Zixin Wang²

Affiliations

¹ Department of Science and Education, Shenzhen Baoan Women's and Children's Hospital, Shenzhen, Guangdong, China.
² Centre for Health Behaviours Research, Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China.
³ Department of Health and Physical Education, The Education University of Hong Kong, Hong Kong, China.
⁴ School of Public Health, Sun Yat-sen University, Shenzhen, China.
⁵ Kirby Institute, University of New South Wales, Sydney, NSW, Australia.
⁶ School of Public Health, Shanghai Jiaotong University School of Medicine, Shanghai, China.
⁷ School of Media and Communication, Shenzhen University, Shenzhen, China.
⁸ Longhua District Center for Disease Control and Prevention, Shenzhen, China.

^# Contributed equally.

Abstract

Introduction: Assessing the likelihood of engaging in high-risk sexual behavior can assist in delivering tailored educational interventions. The objective of this study was to identify the most effective algorithm and assess high-risk sexual behaviors within the last six months through the utilization of machine-learning models.

Methods: The survey conducted in the Longhua District CDC, Shenzhen, involved 2023 participants who were employees of 16 different factories. The data was collected through questionnaires administered between October 2019 and November 2019. We evaluated the model's overall predictive classification performance using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. All analyses were performed using the open-source Python version 3.9.12.

Results: About a quarter of the factory workers had engaged in risky sexual behavior in the past 6 months. Most of them were Han Chinese (84.53%), hukou in foreign provinces (85.12%), or rural areas (83.19%), with junior high school education (55.37%), personal monthly income between RMB3,000 (US$417.54) and RMB4,999 (US$695.76; 64.71%), and were workers (80.67%). The random forest model (RF) outperformed all other models in assessing risky sexual behavior in the past 6 months and provided acceptable performance (accuracy 78%; sensitivity 11%; specificity 98%; PPV 63%; ROC 84%).

Discussion: Machine learning has aided in evaluating risky sexual behavior within the last six months. Our assessment models can be integrated into government or public health departments to guide sexual health promotion and follow-up services.

Keywords: HIV/STIs; logistic regression; machine learning; random forest model; risky sexual behaviors.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Asian People
China
Health Risk Behaviors*
Humans
Machine Learning*
Manufacturing and Industrial Facilities
Occupational Groups* / psychology
Sexual Behavior* / psychology
Sexual Health

Grants and funding

This study was funded by the High-Level Project of Medicine in Longhua, Shenzhen (HLPM201907020105) and the Key Discipline of Infectious Diseases Control and Prevention of Long Hua (Grant 2020-2014).