The prediction and impact of self-criticism on non-suicidal self-injury in adolescents: A study based on machine learning.

Résumé

This study uniquely combines machine learning with structural equation modeling to investigate how self-criticism affects non-suicidal self-injury (NSSI) behaviors in adolescents. Using a Support Vector Machine (SVM) for binary classification (distinguishing NSSI presence/absence) and XGBRegressor for regression (predicting NSSI severity), with self-criticism, emotional regulation difficulties, and public self-consciousness as key predictors, the study accurately classified and forecasted NSSI behaviors. The K-fold cross-validation confirmed the robustness of these predictors. The SVM classifier achieved an accuracy of 85.3% (AUC = 0.89, 95% CI [0.87, 0.91]), while the regression model explained 50.9% of variance (R = 0.509). Structural equation modeling revealed that self-criticism directly influences NSSI (β = 0.32, p