Toward a fair, gender-debiased classifier for the diagnosis of attention deficit/hyperactivity disorder- a Machine-Learning based classification study
Based on methodological characteristics and insights from global and local model explana-tion, we discuss the reasons of these findings and conclude, that using the combination of bias mitigation and model explanation, improved classification models can be achieved.
Authors
- Susanne Neufang (ORCID: https://orcid.org/0000-0002-4927-2969)
- Atae Akhrif (ORCID: https://orcid.org/0009-0002-0801-7096)
- Feifei Li (ORCID: https://orcid.org/0000-0003-4815-8547)
- Oya Beyan (ORCID: https://orcid.org/0000-0001-7611-3501)
Institutions
- University of Cologne (DE)
- Fraunhofer Institute for Applied Information Technology (DE)
- University Hospital Cologne (DE)
Publication Details
- Journal
- BMC Medical Informatics and Decision Making
- Published
- 2025-08-05
- DOI
- https://doi.org/10.1186/s12911-025-03126-0
- Citations
- 1
- Primary Topic
- Attention Deficit Hyperactivity Disorder
- Type
- article
- Field-Weighted Citation Impact
- 1.47
Funders
- Deutsche Forschungsgemeinschaft