Hierarchical clustering analysis & machine learning models for diagnosing skeletal classes I and II in German patients
The KNN model with three key variables demonstrated sufficient accuracy for classifying skeletal classes I and II, supporting efficient and still personalized orthodontic diagnostics and treatment planning. Further studies with balanced sample sizes are needed for validation.
Authors
- Osayd Zohud (ORCID: https://orcid.org/0000-0003-3530-1420)
- Iqbal M. Lone (ORCID: https://orcid.org/0000-0001-5292-6778)
- Sebastian Krohn (ORCID: https://orcid.org/0000-0002-2256-2085)
- Obaida Awadi
- Christian Kirschneck (ORCID: https://orcid.org/0000-0001-9473-8724)
- Fuad A. Iraqi (ORCID: https://orcid.org/0000-0001-5525-206X)
- Peter Proff (ORCID: https://orcid.org/0000-0003-1137-5361)
- Eva Paddenberg (ORCID: https://orcid.org/0000-0001-8207-5257)
- Nezar Watted (ORCID: https://orcid.org/0009-0003-3924-0327)
- Samir Masarwa
- Kareem Midlej (ORCID: https://orcid.org/0000-0003-0144-575X)
Institutions
- University of Bonn (DE)
- Tel Aviv University (IL)
- University Hospital Regensburg (DE)
- Prosperity Now (US)
- Arab American University (PS)
- University of Regensburg (DE)
Publication Details
- Journal
- BMC Oral Health
- Published
- 2025-05-15
- DOI
- https://doi.org/10.1186/s12903-025-06063-6
- Citations
- 1
- Primary Topic
- Dental Radiography and Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.79
Funders
- Tel Aviv University
- Universitätsklinikum Regensburg