Performance measures for ML models.

Suicide is now the 2nd leading cause of death. Despite existing prevention initiatives, the incidence of suicide deaths among younger population has risen in recent years. The study aimed to determine the social and behavioral predictors of suicidal ideation (SI) and suicide attempts (SA) among adolescents and young adults. We analyzed data from the 2021 Youth Risk Behavior Surveillance System (YRBSS) involving 17,232 U.S. students in grades 9–12. Among respondents, 20.85% (3,593) reported suicidal ideation, and 10.17% (1,753) attempted suicide. Of the 3,526 participants with depression, 46.23% (1,630) experienced suicidal ideation, and 19.46% (686) attempted suicide. Among the 8,152 female participants, 27.42% (2,335) reported suicidal ideation, and 13.37% (1,090) reported suicide attempts. Also, 29.06% of 3,981 participants who were overweight experienced SI and 14.02% of them attempted suicide. We developed independent SI and SA prediction models using extreme gradient boosting (XGB), logistic regression (LR), and K-Nearest Neighbors (KNN) algorithms. The LR model demonstrated the best performance in predicting both SI and SA, with an accuracy of 0.74 (for both), specificity of 0.71 for SI; 0.69 for SA, and sensitivity (0.77 for SI; 0.81 for SA). It also achieved high positive predictive values (PPV) of 0.90 for SI; 0.95 for SA, and an area under the Receiver Operating Characteristic (AUROC) curve of 0.82 for both outcomes. The model identified cyberbullying (electronically bullied), drug use, forced sexual intercourse, sexual violence, difficulty in concentrating, and early use of alcohol or marijuana, as some of the most important and common predictors of SI and SA. In conclusion, our research advocates for the urgent development of support systems that address the early signs of suicidal behaviors. It also surfaces the fact that predicting with certainty who is likely to attempt suicide remains a challenge.