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

Journal: Frontiers in public health
PMID: 37601175

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.

Authors

  • Fang Zhang
  • Shiben Zhu
    Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, China.
  • Siyu Chen
    School of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China.
  • Ziyu Hao
    Centre for Health Behaviours Research, Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China.
  • Yuan Fang
    Department of Neurology, Dongyang People's Hospital, Affiliated to Wenzhou Medical University, Dongyang, China.
  • Huachun Zou
    School of Public Health, Sun Yat-sen University, Shenzhen, China.
  • Yong Cai
    State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha 410082, P.R. China.
  • Bolin Cao
    School of Media and Communication, Shenzhen University, Shenzhen, China.
  • Kechun Zhang
    Longhua District Center for Disease Control and Prevention, Shenzhen, China.
  • He Cao
    Department of Computer Science, Harbin Institute of Technology, Shenzhen, Guangdong, China.
  • Yaqi Chen
    Longhua District Center for Disease Control and Prevention, Shenzhen, China.
  • Tian Hu
    Department of Radiology, Yanan University Affiliated Hospital, China.
  • Zixin Wang
    Centre for Health Behaviours Research, Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China.

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