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

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.

Methods

Using a two-step hybrid model combining long short-term memory (LSTM) and machine learning (ML), we compared 20 depression risk/protective factors in a balanced panel dataset of middle-aged and elderly Chinese adults (N = 3706; aged 45-94; 64.65% female; 41.20% middle-

SUBMITTER: Zhang L 

PROVIDER: S-EPMC12004675 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature