Decoding the adolescent non-suicidal self-injury: understanding with interpretable machine learning insights.

ABSTRACT:

Purpose

Non-suicidal self-injury is a common risk behavior in adolescence but is often difficult to detect. This study employs interpretable machine learning techniques to develop a classification model for adolescent non-suicidal self-injury and elucidate pertinent factors. Employing diverse algorithms, a comprehensive analysis is conducted to discern critical risk and protective elements within a large dataset, evaluating their alignment with the Integrated Theoretical Model.

Methods

In partnership with educational institutions in eastern China, this research compiled data on behaviors and correlated factors through the administration of questionnaires, incorporating demographic information and seven validated scales. Analytical models were built using six machine learning

SUBMITTER: Fu H 

PROVIDER: S-EPMC12400739 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature