Screening of Mood Symptoms Using MMPI-2-RF Scales: An Application of Machine Learning Techniques

Journal of Personalized Medicine

卷:11

Hye-Kyung Lee¹  Kounseok Lee²  Sunhae Kim² 

[1] Department of Nursing, College of Nursing and Health, Kongju National University, Kognju 32588, Korea;

[2] Department of Psychiatry, Hanyang University Medical Center, Seoul 04763, Korea;

关键词: Minnesota Multiphasic Personality Inventory;    depression;    bipolar disorders;    risk factors;    machine learning;    Patient Health Questionnaire;   

DOI  :  10.3390/jpm11080812

来源: DOAJ

【 摘 要 】

(1) Background: The MMPI-2-RF is the most widely used and most researched test among the tools for assessing psychopathology, and previous studies have established its validity. Mood disorders are the most common mental disorders worldwide; they present difficulties in early detection, go undiagnosed in many cases, and have a poor prognosis. (2) Methods: We analyzed a total of 8645 participants. We used the PHQ-9 to evaluate depressive symptoms and the MDQ to evaluate hypomanic symptoms. We used the 10 MMPI-2 Restructured Form scales and 23 Specific Problems scales for the MMPI-2-RF as predictors. We performed machine learning analysis using the k-nearest neighbor classification, linear discriminant analysis, and random forest classification. (3) Results: Through the machine learning technique, depressive symptoms were predicted with an AUC of 0.634–0.767, and the corresponding value range for hypomanic symptoms was 0.770–0.840. When using RCd to predict depressive symptoms, the AUC was 0.807, but this value was 0.840 when using linear discriminant classification. When predicting hypomanic symptoms with RC9, the AUC was 0.704, but this value was 0.767 when using the linear discriminant method. (4) Conclusions: Using machine learning analysis, we defined that participants’ mood symptoms could be classified and predicted better than when using the Restructured Clinical scales.

【 授权许可】

Unknown