Identifying minimal risk factors for adolescent suicidal ideation and suicide attempts: A machine learning-optimized approach.

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: Sensitivity 93.49%, Specificity 90.21%

• Primary Outcome: Suicide attempt prediction achieved AUC 97.28%

• Nnt Or Sensitivity: PPV 90.52%, NPV 93.26% for suicide attempt prediction

• Confidence Interval: Not reported

Clinical Application

Requires implementation of standardised screening tools and machine learning infrastructure Could inform RACGP adolescent mental health guidelines and school-based screening programs, though cultural validation needed for Australian adolescent populations Adolescents aged 12-18 years in school-based or community mental health settings

Abstract

This study aimed to develop and validate a machine learning (ML) model to identify the minimal risk factors for adolescent suicidal behaviors, including suicidal ideation and suicide attempts. Data from the Korea Youth Risk Behavior Web-based Survey (2022-2023), including 90,813 adolescents aged 12-18 years, were analyzed. Using multidimensional risk factors spanning sociodemographic, physical and mental health, and behavioral domains, we applied a Random Forest model combined with recursive feature elimination to identify a minimal subset of risk factors (optimal features). Model performance for identifying suicidal ideation and predicting suicide attempts was evaluated via area under the curve (AUC), sensitivity, specificity, and accuracy metrics across the validation datasets. Sadness, loneliness, anxiety, and stress were identified as optimal features, achieving a high AUC, sensitivity, specificity, and accuracy in identifying suicidal ideation and predicting suicide attempts. Additional factors further improved the ML model's predictive performance for suicide attempts, achieving an AUC of 97.28%, sensitivity of 93.49%, specificity of 90.21%, accuracy of 91.85%, a PPV of 90.52%, and an NPV of 93.26%. This study demonstrated the efficacy of ML-driven approaches in identifying critical risk factors for adolescent suicidal behaviors. The findings highlight the potential of ML frameworks to transform suicide prevention strategies and improve mental health outcomes in adolescents.

References

1. 1.Park, C., & Lee, B. C. (2026). Identifying minimal risk factors for adolescent suicidal ideation and suicide attempts: A machine learning-optimized approach. PLoS One. https://doi.org/10.1371/journal.pone.0346050