Objectives: Assessing the risks of youth suicide in educational and clinical settings is crucial.Therefore, this study developed a machine learning model to predict suicide attempts using the Korean Youth Risk Behavior Web-based Survey (KYRBWS). Methods: KYRBWS is conducted annually on Korean middle and high school students to assess their health-related behaviors. The KYRBWS data for 2021, which showed 1206 adolescents reporting suicide attempts out of 54848, was split into the training (n=43878) and test (n=10970) datasets. Thirty-nine features were selected from the KYRBWS questionnaire. The balanced accuracy of the model was employed as a metric to select the best model. Independent validations were conducted with the test dataset of 2021 KYRBWS (n=10970) and the external dataset of 2020 KYRBWS (n=54948). The clinical implication of the prediction by the selected model was measured for sensitivity, specificity, true prediction rate (TPR), and false prediction rate (FPR). Results: Balanced bag of histogram gradient boosting model has shown the best performance (balanced accuracy=0.803). This model shows 76.23% sensitivity, 83.08% specificity, 10.03% TPR, and 99.30% FPR for the test dataset as well as 77.25% sensitivity, 84.62% specificity, 9.31% TPR, and 99.45% FPR for the external dataset, respectively. Conclusion These results suggest that a specific machine learning model can predict suicide attempts among adolescents with high accuracy.

Objectives: Suicide is the leading cause of death among adolescents in South Korea, and depression and personality profiles have been identified as significant risk factors for self-injurious behavior. This study examined the influence of depressive mood and temperament/ character on self-injury in adolescents. Methods: A total of 116 adolescents (aged 12–18 years) with a primary diagnosis of major depressive disorder (MDD) and their parents were enrolled in this study. The participants were divided into three groups based on adolescent’s self-injury frequency, and their Children’s Depression Inventory (CDI), Youth Self-Report (YSR), and Temperament and Character Inventory (TCI) scores were compared. Finally, mediation analysis was conducted to investigate the relationship between suicidal ideation and self-injury. Results: Of study participants, 75.9% answered that they had suicidal ideation, and 55.2% answered that they had engaged in self-injurious behavior in the last six months. There were significant differences in CDI and suicidal ideation among the groups. After adjusting for age and sex, mediation analysis indicated that depressive mood mediated the relationship between suicidal ideation and self-injury. Conclusion This study emphasizes the importance of evaluating and managing depressive mood severity in adolescents with MDD as these factors partially mediate the transition from suicidal ideation to self-injury.