Explainable Multi-Layer Dynamic Ensemble Framework Optimized for Depression Detection and Severity Assessment
This study emphasizes the potential of dynamic ensemble learning in mental health assessments, particularly in detecting and evaluating depression severity. The findings provide a strong foundation for future use of dynamic ensemble frameworks in mental health assessments, demonstrating their potential for practical clinical applications.
Authors
- Tamer Abuhmed (ORCID: https://orcid.org/0000-0001-9232-4843)
- Meshal Alharbi (ORCID: https://orcid.org/0000-0002-6025-7858)
- Shaker El–Sappagh (ORCID: https://orcid.org/0000-0001-9705-1477)
- Dillan Imans
Institutions
- Suez University (EG)
- Prince Sattam Bin Abdulaziz University (SA)
- Benha University (EG)
- Galala University
- Sungkyunkwan University (KR)
Publication Details
- Journal
- Diagnostics
- Published
- 2024-10-25
- DOI
- https://doi.org/10.3390/diagnostics14212385
- Citations
- 6
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 2.08
Funders
- King Salman Center for Disability Research