Abstract
Traditional clinical risk assessment tools proved inadequate for reliably identifying individuals at high risk for suicidal behavior. As a result, machine learning (ML) techniques have become progressively incorporated into psychiatric care. This study evaluates the predictive capability of national survey data, which includes factors such as lifestyle behaviors and mental health indicators, in forecasting adolescent suicidal behavior. The predictive performance of six ML models-Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), Gradient Boosting Machine (GBM), Extremely Randomized Trees (ET), and Distributed Random Forest (DRF)-was systematically compared. Employed SHapley Additive exPlanations (SHAP) values and Permutation Feature Importance (PFI) for interpretability analysis, and ultimately utilized interaction analysis to examine the complex interrelationships among key variables associated with suicide risk. Both the Expert Consultation Method (ECM) and Random Forest-Based Filter Feature Selection (RFFS) datasets revealed that the GBM model achieved the best results, with a predictive accuracy (ACC) of 88%, sensitivity (SENS) of 97%, specificity (SPEC) of 26%, positive predictive value (PPV) of 90%, negative predictive value (NPV) of 56%, and an area under the curve (AUC) of 83%. Feature importance analysis identified stress and depression as the most significant determinants of suicidal ideation and behavior in middle and high school students, respectively. Multivariate interaction effect analysis further revealed that, at higher levels of depression, lower anxiety levels were significantly correlated with a reduced probability of suicide risk. Additionally, a positive association between stress and anxiety was observed. Overall, the integration of advanced computational techniques with national survey data moderately enhances the accuracy of suicide risk prediction, providing a strong empirical foundation for early intervention in adolescent suicidal behavior.