Modeling Personality–Emotion–Somatic Symptom Pathways with Explainable Machine Learning - Torres, Mariana; Peixoto , Fabricio; Martínez-Pacheco , Jaime Martínez-Pacheco (Journal of Personality and Psychosomatic Research (JPPR), 2026)
This study aimed to examine the pathways linking personality traits, emotional distress, and somatic symptoms using explainable machine learning techniques. This cross‑sectional study included 1,024 adults from Mexico (54.3% women; mean age = 34.7 years, SD = 11.2). Participants completed validated psychological instruments, including the Big Five Inventory (BFI‑44) to assess personality traits, the Depression Anxiety Stress Scales (DASS‑21) to measure emotional distress, and the Patient Health Questionnaire Somatic Symptom Scale (PHQ‑15) to assess somatic symptom severity. Descriptive statistics and Pearson correlations were first conducted to examine relationships among variables. Subsequently, machine learning models—Random Forest, Gradient Boosting, and XGBoost—were developed to predict somatic symptoms. Model performance was evaluated using the coefficient of determination (R²), and SHapley Additive exPlanations (SHAP) were used to identify the relative importance of predictors and interpret the models. Correlation analyses indicated significant positive associations between neuroticism, depression, anxiety, stress, and somatic symptoms (r range = .29–.48, p < .001). Neuroticism showed the strongest correlations with anxiety (r = .48) and somatic symptoms (r = .41). Among the machine learning models, XGBoost demonstrated the best predictive performance (R² = 0.42), followed by Gradient Boosting (R² = 0.39) and Random Forest (R² = 0.36). SHAP analyses revealed that anxiety and neuroticism were the most influential predictors of somatic symptoms, followed by stress and depression. Interaction analysis suggested that individuals with high neuroticism combined with high anxiety exhibited nearly twice the predicted level of somatic symptom severity compared to individuals with low scores on these variables. The findings highlight the central role of emotional distress and neuroticism in predicting somatic symptoms and demonstrate the value of explainable machine learning for identifying complex psychosomatic pathways.