Machine learning models for prognosis prediction in regenerative endodontic procedures
RF and GBM models outperformed XGB, logR, SVM models on the overall performance on our datasets, exhibiting the potential capability to predict the prognosis of REPs. The ranking of feature importance contributes to establishing the scoring system for prognosis prediction in REPs, assisting clinicians in decision-making.
Authors
- Dali Zheng (ORCID: https://orcid.org/0000-0003-2345-4040)
- Kaizhi Chen (ORCID: https://orcid.org/0000-0003-2859-8924)
- Jun Yao (ORCID: https://orcid.org/0000-0002-1088-1548)
- Youguang Lu (ORCID: https://orcid.org/0000-0002-1325-3790)
- Bill Kahler (ORCID: https://orcid.org/0000-0002-4181-3871)
- Qianqian Cai (ORCID: https://orcid.org/0000-0003-1661-7075)
- Yanjun Zhang (ORCID: https://orcid.org/0000-0003-3095-625X)
- Jing Lü
Institutions
- The University of Sydney (AU)
- Fujian Medical University (CN)
- Fuzhou University (CN)
Publication Details
- Journal
- BMC Oral Health
- Published
- 2025-02-13
- DOI
- https://doi.org/10.1186/s12903-025-05531-3
- Citations
- 6
- Primary Topic
- Dental Radiography and Imaging
- Type
- article
- Field-Weighted Citation Impact
- 10.74
Funders
- Sun Yat-sen University
- Natural Science Foundation of Fujian Province
- Fujian Medical University
- Fujian Provincial Health Technology Project