Construction and validation of machine learning algorithm for predicting depression among home-quarantined individuals during the large-scale COVID-19 outbreak: based on Adaboost model.

Journal: BMC psychology
PMID: 38659077

Abstract

OBJECTIVES: COVID-19 epidemics often lead to elevated levels of depression. To accurately identify and predict depression levels in home-quarantined individuals during a COVID-19 epidemic, this study constructed a depression prediction model based on multiple machine learning algorithms and validated its effectiveness.

Authors

  • Yiwei Zhou
    Business School, University of Shanghai for Science and Technology, 200093, Shanghai, China.
  • Zejie Zhang
    Wenzhou Center for Disease Control and Prevention, 325000, Wenzhou, China.
  • Qin Li
    Department of Spine Surgery, The Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang, China.
  • Guangyun Mao
    Department of Preventive Medicine, School of Public Health, Wenzhou Medical University, 325035, Wenzhou, China.
  • Zumu Zhou
    The Affiliated Kangning Hospital of Wenzhou Medical University Zhejiang Provincial Clinical Research Center for Mental Disorders, 325007, Wenzhou, China. zhouzumu@126.com.

Keywords

External Resources

Popular Topics
Recent Journals