Exploring Informative Items for Bipolar Disorder Classification Using Machine Learning With Anger Coping Styles in Combination With the Mood Disorder Questionnaire and Bipolar Spectrum Diagnostic Scale
Integrating anger coping styles with mood symptoms enhanced diagnostic accuracy, particularly when items related to undesirable behaviors were excluded. This machine learning approach shows potential for effectively evaluating bipolarity and underscores the importance of refining self-report scales to optimize diagnostic tools. Future research should incorporate clinical and objective data to enhance classification models.
Authors
- Hyun Ju Lim (ORCID: https://orcid.org/0000-0002-8962-8173)
- Hwagyu Suh (ORCID: https://orcid.org/0000-0002-0360-6708)
- Byung Dae Lee (ORCID: https://orcid.org/0000-0003-4295-8463)
- Eunsoo Moon (ORCID: https://orcid.org/0000-0002-8863-3413)
- Hyunji Lee (ORCID: https://orcid.org/0000-0003-4375-2694)
- Kyungwon Kim (ORCID: https://orcid.org/0000-0001-8947-9078)
- Young Min Lee (ORCID: https://orcid.org/0000-0001-7483-7143)
Institutions
- Pusan National University Hospital (KR)
- Pusan National University (KR)
Publication Details
- Journal
- Psychiatry Investigation
- Published
- 2025-12-05
- DOI
- https://doi.org/10.30773/pi.2025.0108
- Primary Topic
- Bipolar Disorder and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Pusan National University