Prediction model for depression risk in middle-aged and elderly patients with metabolic syndrome: a nomogram and interpretable machine learning approach based on CHARLS.

ABSTRACT:

Background

Individuals with metabolic syndrome (MetS) are more prone to depression, which is a significant complication impacting quality of life. This research seeks to create and validate predictive models for assessing depression risk in patients with MetS.

Methods

Data from the 2011 (baseline) and 2015 waves of the China Health and Retirement Longitudinal Study (CHARLS) were employed in this study. By excluding variables with more than 20% missing values, 38 features, such as demographic information, lifestyle factors, comorbidities, health status indicators, and blood test information, were included. The Least Absolute Shrinkage and Selection Operator (LASSO) identified 11 key contributors, and 6 machine learning (ML) models were employed to determine the best depressio

SUBMITTER: Chen J 

PROVIDER: S-EPMC12522473 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature