Leveraging data augmentation for machine learning models in predicting depression and anxiety using the Revised Child Anxiety and Depression Scale clinical reports
The findings suggest that the Random Forest algorithm using 46 features suits the data well and has the potential to be further developed as a decision support system for the concerned professionals and improve the usual way of screening anxiety and depression in children and adolescents.
Authors
- Syeda Aneela Zahra Shamsi
- Rehan Zafar Paracha (ORCID: https://orcid.org/0000-0003-4227-1354)
- Saleha Noor (ORCID: https://orcid.org/0000-0002-0043-4189)
- Zamir Hussain (ORCID: https://orcid.org/0000-0001-5020-9169)
- Qurrat Ulain Hamdan (ORCID: https://orcid.org/0000-0002-0618-6796)
- Maseeh uz Zaman (ORCID: https://orcid.org/0000-0001-9586-7034)
Institutions
- University of Padua (IT)
- Rawalpindi Medical University (PK)
- Riphah International University (PK)
- National Institute of Health (PK)
- National University of Sciences and Technology (PK)
Publication Details
- Journal
- Frontiers in Psychiatry
- Published
- 2025-11-27
- DOI
- https://doi.org/10.3389/fpsyt.2025.1672178
- Primary Topic
- Mental Health via Writing
- Type
- article
- Field-Weighted Citation Impact
- 0.00