Predicting depression and unravelling its heterogeneous influences in middle-aged and older people populations: a machine learning approach.

Journal: BMC psychology
PMID: 40247342

Abstract

BACKGROUND: Aging has become a global trend, and depression, as an accompanying issue, poses a significant threat to the health of middle-aged and older adults. Existing studies primarily rely on statistical methods such as logistic regression for small-scale data analysis, while research on the application of machine learning in large-scale data remains limited. Therefore, this study employs machine learning methods to explore the risk factors for depression among middle-aged and older adults in China.

Authors

  • Ling Zhang
  • Ruigang Wei
    School of Software and Internet of Things, Jiangxi University of Finance and Economics, Nanchang, China. 2202220831@stu.jxufe.edu.cn.
  • Jingwen Zhou
    Science Center for Future Foods, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China; Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China; Jiangsu Province Engineering Research Center of Food Synthetic Biotechnology, Jiangnan University, Wuxi 214122, China; Jiangsu Province Basic Research Center for Synthetic Biology, Jiangnan University, Wuxi 214122, China. Electronic address: zhoujw1982@jiangnan.edu.cn.
  • Lin Tan
    School of Software and Internet of Things, Jiangxi University of Finance and Economics, Nanchang, China.
  • Xiaolong Che
    School of Software and Internet of Things, Jiangxi University of Finance and Economics, Nanchang, China.
  • Minqinag Zhang
    School of Software and Internet of Things, Jiangxi University of Finance and Economics, Nanchang, China.
  • Xiaoyue Ning
    School of Software and Internet of Things, Jiangxi University of Finance and Economics, Nanchang, China.
  • Zhiliang Zhong
    School of Software and Internet of Things, Jiangxi University of Finance and Economics, Nanchang, China.

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