An interpreting machine learning models to predict amputation risk in patients with diabetic foot ulcers: a multi-center study
The XGBoost algorithm-based prediction model can be used to dynamically estimate the risk of LEA in DFU patients, making it a valuable tool for preventing the progression of DFUs to amputation.
Authors
- Lili You (ORCID: https://orcid.org/0000-0001-6815-5939)
- Meng Ren (ORCID: https://orcid.org/0000-0001-9935-1449)
- B.S. Yuan (ORCID: https://orcid.org/0009-0009-9593-5688)
- Shan Xiao (ORCID: https://orcid.org/0000-0003-1603-0338)
- Haoran Tao (ORCID: https://orcid.org/0009-0007-4564-516X)
- Yan Li (ORCID: https://orcid.org/0000-0002-6674-2483)
- Yu Chen (ORCID: https://orcid.org/0000-0001-9459-9349)
- Yuhan Huang (ORCID: https://orcid.org/0000-0002-7950-3895)
- Dan Liu (ORCID: https://orcid.org/0009-0004-5649-3948)
Institutions
- Sun Yat-sen University (CN)
- People 's Hospital of Jilin Province (CN)
- Sun Yat-sen Memorial Hospital (CN)
- Dongguan People’s Hospital (CN)
- Shantou Central Hospital (CN)
- Shenzhen Second People's Hospital (CN)
Publication Details
- Journal
- Frontiers in Endocrinology
- Published
- 2025-03-25
- DOI
- https://doi.org/10.3389/fendo.2025.1526098
- Citations
- 7
- Primary Topic
- Diabetic Foot Ulcer Assessment and Management
- Type
- article
- Field-Weighted Citation Impact
- 9.06