Electroencephalogram (EEG) Based Prediction of Attention Deficit Hyperactivity Disorder (ADHD) Using Machine Learning
EEG-based machine learning models, such as the XGBoost classifier, show potential as non-invasive tools for ADHD diagnosis, offering high accuracy and interpretability. The novelty of this approach lies in combining SHAP analysis with data augmentation techniques and LOSO cross-validation, ensuring both explainability and robust generalizability. Future research with larger datasets and diverse populations is recommended to validate findings and explore clinical applications.
Authors
- Bung-Nyun Kim (ORCID: https://orcid.org/0000-0002-2403-3291)
- Chan-Mo Yang (ORCID: https://orcid.org/0000-0002-4959-7595)
- Johanna Inhyang Kim (ORCID: https://orcid.org/0000-0002-2367-0934)
- Jaehyung Kwon (ORCID: https://orcid.org/0000-0002-0159-394X)
- Jun Won Kim (ORCID: https://orcid.org/0000-0001-6793-3006)
Institutions
- Seoul National University Hospital (KR)
- Daegu Catholic University (KR)
- Hanyang University Medical Center (KR)
- Wonkwang University (KR)
Publication Details
- Journal
- Neuropsychiatric Disease and Treatment
- Published
- 2025-02-01
- DOI
- https://doi.org/10.2147/ndt.s509094
- Citations
- 8
- Primary Topic
- Attention Deficit Hyperactivity Disorder
- Type
- article
- Field-Weighted Citation Impact
- 11.77
Funders
- Korea Health Industry Development Institute