Identification of depressive symptoms in adolescents using machine learning combining childhood and adolescence features.

ABSTRACT:

Background

Depressive symptoms in adolescents can significantly affect their daily lives and pose risks to their future development. These symptoms may be linked to various factors experienced during both childhood and adolescence. Machine learning (ML) has attracted substantial attention in the field of adolescent depression; however, studies establishing prediction models have primarily considered childhood or adolescent features separately, resulting in a lack of analyses that incorporate factors from both stages.

Methods

We collected 39 features related to childhood and adolescence. Using the maximum relevance-minimum redundancy method and four ML algorithms, we determined the optimal feature subset for identifying depressive symptoms and constructed child-adolescent mod

SUBMITTER: Liu X 

PROVIDER: S-EPMC11752660 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature