Exploring key factors influencing depressive symptoms among middle-aged and elderly adult population: A machine learning-based method.

Journal: Archives of gerontology and geriatrics
PMID: 39369564

Abstract

OBJECTIVE: This paper aims to investigate the key factors, including demographics, socioeconomics, physical well-being, lifestyle, daily activities and loneliness that can impact depressive symptoms in the middle-aged and elderly population using machine learning techniques. By identifying the most important predictors of depressive symptoms through the analysis, the findings can have important implications for early depression detection and intervention.

Authors

  • Thu Tran
    School of Computing and Information Systems, Singapore Management University, Singapore. Electronic address: ndttran.2019@phdcs.smu.edu.sg.
  • Yi Zhen Tan
    School of Computing and Information Systems, Singapore Management University, Singapore.
  • Sapphire Lin
    Centre for Population Health Research and Implementation, SingHealth, Singapore; Duke-NUS Medical School, Singapore.
  • Fang Zhao
    St. John Fisher College, Rochester, NY, USA.
  • Yee Sien Ng
    Duke-NUS Medical School, Singapore; Singapore General Hospital, SingHealth, Singapore.
  • Dong Ma
    Department of Chemistry, Zhejiang University Hangzhou 310027 P. R. China wujunwu@zju.edu.cn +86-571-87951895.
  • JeongGil Ko
    Department of Computer Engineering, Ajou University, Suwon, Republic of Korea.
  • Rajesh Balan
    School of Computing and Information Systems, Singapore Management University, Singapore.

Keywords

External Resources

Popular Topics
Recent Journals