Evaluation of four machine learning methods in predicting orthodontic extraction decision from clinical examination data and analysis of feature contribution
Among the machine learning models that only use clinical data for tooth extraction prediction, decision tree has the best overall performance. For tooth extraction decisions, specifically, crowding, lateral facial profile, and lower incisor inclination have the greatest contribution.
Authors
- Zhixian Wang (ORCID: https://orcid.org/0000-0002-0515-8312)
- Jialiang Huang (ORCID: https://orcid.org/0000-0002-5218-1144)
- Yichen Pan (ORCID: https://orcid.org/0009-0005-8597-0704)
- Xiaoyi Ding (ORCID: https://orcid.org/0000-0001-8842-7604)
- Ying Jin (ORCID: https://orcid.org/0009-0009-5738-8572)
- Chris Yang (ORCID: https://orcid.org/0009-0007-7657-3461)
- I Lei Chan
Institutions
- Shanghai Jiao Tong University (CN)
- Fudan University (CN)
- National Clinical Research Center for Digestive Diseases (CN)
- Shanghai Stomatological Hospital (CN)
- Shanghai Ninth People's Hospital (CN)
- Shanghai University of Medicine and Health Sciences (CN)
Publication Details
- Journal
- Frontiers in Bioengineering and Biotechnology
- Published
- 2024-10-14
- DOI
- https://doi.org/10.3389/fbioe.2024.1483230
- Citations
- 11
- Primary Topic
- Dental Radiography and Imaging
- Type
- article
- Field-Weighted Citation Impact
- 5.43
Funders
- National Natural Science Foundation of China