Modeling the Contribution of Cognitive Fusion, Rumination, and Worry to Psychosomatic Distress: An Explainable Machine Learning Study
Keywords:
Psychosomatic distress, Cognitive fusion, Rumination, Worry, Explainable machine learningAbstract
The present study aimed to model the unique and interactive contributions of cognitive fusion, rumination, and worry to psychosomatic distress within a Tunisian adult sample by applying and interpreting an explainable machine learning framework. A cross-sectional design was utilized to evaluate 1254 adult participants (M_age=32.4, SD=8.7) from Tunisia, recruited via snowball sampling. Self-report data were collected using the Cognitive Fusion Questionnaire (CFQ), Ruminative Responses Scale (RRS), Penn State Worry Questionnaire (PSWQ), and Patient Health Questionnaire-15 (PHQ-15). The data analysis employed an advanced machine learning pipeline, comparing Random Forest, Support Vector Regression, and eXtreme Gradient Boosting (XGBoost) algorithms. The optimal model was subsequently interpreted using the SHapley Additive exPlanations (SHAP) framework to extract global feature importance, non-linear thresholds, and interaction effects. Bivariate correlations demonstrated significant positive associations between all cognitive variables and psychosomatic distress (r=0.51 to 0.62). The XGBoost algorithm achieved the highest predictive performance (R^2=0.463, RMSE=2.41). SHAP global feature importance analysis ranked Worry as the top predictor of psychosomatic distress (Mean ∣SHAP∣=1.45), followed sequentially by Rumination and Cognitive Fusion. Furthermore, SHAP dependence plots revealed critical non-linear dynamics, identifying a specific threshold where worry scores exceeding 55 exponentially accelerated physical distress, as well as a significant interaction effect indicating that high cognitive fusion severely amplifies the detrimental somatic impact of rumination. Treating severe psychosomatic distress requires targeted interventions that rapidly reduce worry below critical severity thresholds and utilize cognitive defusion strategies to break the compounding cycle of rumination and psychological entanglement.
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