Diagnosis of Pain Deception Using Minnesota Multiphasic Personality Inventory-2 Based on XGBoost Machine Learning Algorithm: A Single-Blinded Randomized Controlled Trial

Background and Objectives: Assessing pain deception is challenging due to its subjective nature. The main goal of this study was to evaluate the diagnostic value of pain deception using machine learning (ML) analysis with the Minnesota Multiphasic Personality Inventory-2 (MMPI-2) scales, considering accuracy, precision, recall, and f1-score as diagnostic parameters. Materials and Methods: This study was a single-blinded, randomized controlled trial. Subjects were randomly allocated into a non-deception (ND) group and a deception (D) group. Pain deception, as a form of psychological intervention, was taught to subjects in the D group to deceive the physician. MMPI-2, Waddell's sign, and salivary alpha-amylase (SAA) were also measured. For analyzing the MMPI-2, the XGBoost ML algorithm was applied. Results: Of a total of 96 participants, 50 and 46 were assigned to the ND group and the D group, respectively. In the logistic regression analysis, pain and MMPI-2 did not show diagnostic value. However, in the ML analysis, values of the selected MMPI-2 (sMMPI-2) scales related to pain deception showed an accuracy of 0.724, a precision of 0.692, a recall of 0.692, and an f1-score of 0.692. Conclusions: Using MMPI-2 test results, ML can diagnose pain deception better than the conventional logistic regression analysis method by considering different scales and patterns together.