When predicting hypomanic symptoms with RC9, the AUC was 0.704, but this value was 0.767 when using the linear discriminant method. When using RCd to predict depressive symptoms, the AUC was 0.807, but this value was 0.840 when using linear discriminant 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. We performed machine learning analysis using the k-nearest neighbor classification, linear discriminant analysis, and random forest classification. We used the 10 MMPI-2 Restructured Form scales and 23 Specific Problems scales for the MMPI-2-RF as predictors. We used the PHQ-9 to evaluate depressive symptoms and the MDQ to evaluate hypomanic symptoms. (2) Methods: We analyzed a total of 8645 participants. 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. (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.
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