Using machine learning to predict depression among middle-aged and elderly population in China and conducting empirical analysis

The stacked ensemble approach serves as an effective tool for identifying depression in a large population of middle-aged and elderly individuals in China. For depression prediction, factors such as life satisfaction, self-reported health, pain, sleep duration, and cognitive function are identified as highly significant predictive factors.

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Publication Details

Journal
PLoS ONE
Published
2025-03-18
DOI
https://doi.org/10.1371/journal.pone.0319232
Citations
3
Primary Topic
Mental Health Research Topics
Type
article
Field-Weighted Citation Impact
6.35
Controls
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article

Using machine learning to predict depression among middle-aged and elderly population in China and conducting empirical analysis

Ni Jia, Zhe Wang
3 citations
PLoS ONE
Mental Health Research Topics
6.35
article

Using machine learning to predict depression among middle-aged and elderly population in China and conducting empirical analysis

Ni Jia, Zhe Wang
article en
3 citations

Abstract

The stacked ensemble approach serves as an effective tool for identifying depression in a large population of middle-aged and elderly individuals in China. For depression prediction, factors such as life satisfaction, self-reported health, pain, sleep duration, and cognitive function are identified as highly significant predictive factors.

PLoS ONEVol. 20(3)
Shaanxi University of Chinese Medicine (CN)
Openalex Percentile: Top 5%
Mental Health Research Topics
6.35
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