Modeling the Influence of Fear‑Avoidance Beliefs and Cognitive Biases on Psychosomatic Symptom Maintenance Using Machine Learning

Abstract

This study aimed to model the influence of fear‑avoidance beliefs and cognitive biases on psychosomatic symptom maintenance using machine learning techniques. A cross‑sectional study was conducted among 512 adults recruited from the general population in Romania. Psychosomatic symptom severity was assessed using the Patient Health Questionnaire‑15 (PHQ‑15), fear‑avoidance beliefs were measured with the Fear‑Avoidance Beliefs Questionnaire (FABQ), and health anxiety and cognitive bias related to bodily sensations were assessed using the Health Anxiety Inventory and a cognitive bias measure. Data were analyzed using several machine learning algorithms, including Random Forest, Gradient Boosting, Support Vector Regression, and Linear Regression. Model performance was evaluated using mean squared error (MSE), root mean squared error (RMSE), and the coefficient of determination (R²), and feature importance analysis was conducted to identify the most influential predictors of psychosomatic symptom severity. After data screening, the final sample consisted of 498 participants (mean age = 36.8 ± 11.4 years). The mean psychosomatic symptom score measured by the PHQ‑15 was 10.9 ± 5.7. Significant positive correlations were observed between psychosomatic symptoms and health anxiety (r = 0.61), cognitive bias (r = 0.57), and fear‑avoidance beliefs (r = 0.52). Among the machine learning models, the Random Forest algorithm demonstrated the best predictive performance, explaining 68% of the variance in psychosomatic symptom severity (R² = 0.68). Feature importance analysis indicated that health anxiety, cognitive bias, and fear‑avoidance beliefs were the most influential predictors of psychosomatic symptoms. Fear‑avoidance beliefs and cognitive biases play a significant role in the maintenance of psychosomatic symptoms.