Machine‑Learning Prediction of Non‑Suicidal Self‑Injury Based on Emotion Dysregulation Facets, Alexithymia, Impulsivity, and Online Social Interaction Patterns
Keywords:
Non-suicidal self-injury, Machine Learning, Emotion Dysregulation, Alexithymia, Impulsivity, Online Social InteractionAbstract
Objective: This study aimed to develop and validate a high-accuracy machine-learning model to predict non-suicidal self-injury (NSSI) by integrating psychological variables (emotion dysregulation, alexithymia, impulsivity) and digital behavior patterns.
Methods and Materials: A cross-sectional study was conducted with one thousand two hundred forty-eight individuals ( ) aged fifteen to twenty-nine. Participants completed validated self-report measures assessing NSSI (Inventory of Statements About Self-Injury), emotion dysregulation (DERS), alexithymia (TAS-20), impulsivity (UPPS-P), and online social interaction patterns. The dataset was partitioned into training ( ) and testing ( ) sets. Three machine-learning models (Support Vector Machines, Random Forest, and XGBoost) were trained and evaluated, with feature importance analyzed using SHapley Additive exPlanations (SHAP).
Findings: The prevalence of lifetime NSSI was ( ). The eXtreme Gradient Boosting (XGBoost) model demonstrated superior predictive performance on the testing set, achieving an accuracy of and an Area Under the Receiver Operating Characteristic Curve (AUC-ROC) of . SHAP analysis identified the most influential predictors of NSSI as the negative emotional valence of online interactions ( ), impulse control difficulties ( ), and negative urgency ( ).
Conclusion: Machine-learning models can accurately predict non-suicidal self-injury, highlighting the critical role of digital emotional distress and impulsivity as primary risk factors.
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References
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