Comparison of Predictive Models for Keloid Recurrence Based on Machine Learning
This study established three prediction models for keloid recurrence using machine learning techniques, highlighting the significance of KAAS, blood pressure levels, postoperative complications, and inflammatory cell proportions. When compared from various dimensions, the logistic regression model demonstrated the most favorable prognostic performance in terms of AUC.
Authors
- Yijun Xia (ORCID: https://orcid.org/0000-0002-5150-3965)
- Xinwen Kuang (ORCID: https://orcid.org/0000-0002-6876-7844)
- Youbin Wang
- Kexin Song (ORCID: https://orcid.org/0009-0001-3199-0428)
- Hao Yan (ORCID: https://orcid.org/0000-0002-4322-7323)
- Hao Liu (ORCID: https://orcid.org/0000-0002-8687-3237)
- Mengjie Shan
Institutions
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Peking Union Medical College Hospital (CN)
Publication Details
- Journal
- Journal of Cosmetic Dermatology
- Published
- 2025-02-01
- DOI
- https://doi.org/10.1111/jocd.70008
- Citations
- 4
- Primary Topic
- Dermatologic Treatments and Research
- Type
- article
- Field-Weighted Citation Impact
- 8.02
Funders
- Peking Union Medical College Hospital
- Peking Union Medical College