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EP142 Diagnosis of pain deception using MMPI-2 based on XGBoost machine learning algorithm: a single-blinded randomized controlled trial
  1. Ho Sik Moon and
  2. Sung-Jun Kim
  1. Department of Anesthesiology and Pain Medicine, Eunpyeong St. Mary’s Hospital, Seoul, Korea


Background and Aims Assessing pain deception is challenging due to its subjective nature. This study explores using Minnesota Multiphasic Personality Inventory-2 (MMPI-2) analysis with machine learning (ML) to detect malingering. We hypothesize that ML analysis of MMPI-2 can detect pain deception. The main goal of this study was to evaluate the diagnostic value for pain deception using ML analysis with MMPI-2 scales, considering accuracy, precision, recall, and f1-score as diagnostic parameters.

Methods We conducted a single-blinded, randomized controlled trial to evaluate the diagnostic value of the MMPI-2, Waddell’s sign, and salivary alpha amylase (SAA). We grouped the non-deception (ND) group and the deception (D) group randomly.

Results Of the total of 96 participants, 46 were assigned to group D and 50 to group ND. In the logistic regression analysis, pain and MMPI-2 did not show diagnostic value, however in ML analysis, values of selected MMPI-2 (sMMPI-2) which is related to malingering showed accuracy 0.684, precision 0.667, recall 0.800, and f1-score came out as 0.727. When performed with whole MMPI-2(wMMPI-2), accuracy 0.621, precision 0.692, recall 0.562, and f1-score 0.651 was showed. The f1-score was higher in sMMPI-2.

Conclusions We suggest that the diagnosis of pain deception through the pattern changes of MMPI-2 scales using ML could be valuable. It could be a benefit to clinicians to detect deception exactly and objectively in various situations. Further large-scale studies would be needed to screen and predict more precisely

Abstract EP142 Table 1

Descriptive statistics according to the group

Abstract EP142 Table 2

Diagnostic value of exaggeration scales and somatic inconvenience scales of MMPI-2, and Waddell’s sign

Abstract EP142 Table 3

XGBoost analysis of MMPI-2 scales to classify the D and ND group

Institutional Review Board

  • pain deception
  • MMPI-2
  • machine learning
  • XGBoost
  • malingering

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