Diagnosis of Pain Deception Using Machine Learning
Study Overview
This study aimed to improve the diagnosis of pain deception, a complex issue due to its subjective nature. We utilized the Minnesota Multiphasic Personality Inventory-2 (MMPI-2) alongside a machine learning (ML) method called XGBoost to assess its effectiveness.
Study Design
We conducted a single-blinded, randomized controlled trial with 96 participants, divided into two groups: a non-deception (ND) group and a deception (D) group. The D group was trained to mislead the physician about their pain levels. We measured various factors, including MMPI-2 scores and salivary alpha-amylase (SAA).
Key Findings
In our analysis:
- Logistic regression did not show significant diagnostic value for pain and MMPI-2.
- Using the ML analysis, we found that specific MMPI-2 scales related to pain deception had:
- Accuracy: 72.4%
- Precision: 69.2%
- Recall: 69.2%
- F1-score: 69.2%
This indicates that ML can assess pain deception more effectively than traditional methods by analyzing various scales together.
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