Predicting Somatic Symptom Disorder Using Facets of Neuroticism, Somatosensory Amplification, and Interoceptive Sensitivity: An Explainable Machine Learning Analysis
Keywords:
Somatic Symptom Disorder, Explainable Machine Learning, SHAP, Somatosensory Amplification, Interoceptive Sensitivity, NeuroticismAbstract
The objective of this study was to utilize an explainable machine learning approach to predict Somatic Symptom Disorder by delineating the complex, non-linear contributions of specific neuroticism facets, somatosensory amplification, and multidimensional interoceptive sensitivity. A quantitative, cross-sectional design was employed with a sample of Malaysian adults. Data were collected using the Patient Health Questionnaire-15 (PHQ-15), the Neuroticism domain of the NEO-PI-R, the Somatosensory Amplification Scale (SSAS), and the Multidimensional Assessment of Interoceptive Awareness (MAIA). Predictive modeling was conducted using advanced ensemble tree-based algorithms (Random Forest, Gradient Boosting, and XGBoost), and model interpretability was achieved using SHapley Additive exPlanations (SHAP) to evaluate the marginal contribution of each psychological feature. The XGBoost algorithm demonstrated superior predictive performance, achieving an overall accuracy of and an Area Under the Curve (AUC) of . SHAP explainability metrics revealed that Somatosensory Amplification was the most profound positive predictor of somatic distress, followed by the specific Anxiety and Vulnerability facets of neuroticism. Conversely, adaptive interoceptive capacities, specifically the “Not-Worrying” and “Body Trusting” dimensions, emerged as the strongest negative (protective) predictors against Somatic Symptom Disorder classification. Explainable machine learning demonstrates that specific sensory appraisal mechanisms and interoceptive deficits are more critical than broad personality domains in driving somatic distress, providing highly specific targets for precision psychological interventions.
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