Machine learning based identification of suicidal ideation using non-suicidal predictors in a university mental health clinic.

ABSTRACT: Suicide causes over 700,000 deaths annually worldwide. Mental disorders are closely linked to suicidal ideation, but predicting suicide remains complex due to the multifaceted nature of contributing factors. Traditional assessment tools often fail to capture the interactions that drive suicidal thoughts, underscoring the need for more sophisticated predictive approaches. This study aimed to predict suicidal and self-harm ideation among university students using machine learning models without relying on suicidal behavior related predictors. The goal was to uncover less obvious risk factors and provide deeper insights into the complex relationships between psychiatric symptoms and suicidal ideation. Data from 924 university students seeking mental health services were analyzed using seven m

SUBMITTER: Ballı M 

PROVIDER: S-EPMC12015358 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature