Using a machine learning analysis of socio-ecological and psychological factors to predict suicide risk among a nationally representative sample of Ghanaian adolescents.

ABSTRACT: Background. Despite the growing recognition of adolescent suicide as a pressing concern, traditional methods for identifying suicide risk often fail to capture the complex interplay of socio-ecological and psychological factors. The advent of machine learning (ML) offers a transformative opportunity to improve suicide risk prediction and intervention strategies. Objective. This study aims to utilize ML techniques to analyze socio-ecological and psychological risk factors to predict suicide ideation, plans and attempts among a nationally representative sample of Ghanaian adolescents. Methods. A cross-sectional survey was conducted with 1,703 adolescents aged 12-18 years across Ghana measuring psychological factors (depression symptoms, anxiety symptoms etc) and socio-ec

SUBMITTER: Azasu EK 

PROVIDER: S-EPMC12641294 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature