Identifying the most crucial factors associated with depression based on interpretable machine learning: a case study from CHARLS.

ABSTRACT:

Background

Depression is one of the most common mental illnesses among middle-aged and older adults in China. It is of great importance to find the crucial factors that lead to depression and to effectively control and reduce the risk of depression. Currently, there are limited methods available to accurately predict the risk of depression and identify the crucial factors that influence it.

Methods

We collected data from 25,586 samples from the harmonized China Health and Retirement Longitudinal Study (CHARLS), and the latest records from 2018 were included in the current cross-sectional analysis. Ninety-three input variables in the survey were considered as potential influential features. Five machine learning (ML) models were utilized, including CatBoost and eXtreme Gradie

SUBMITTER: Li R 

PROVIDER: S-EPMC11306142 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature