Machine learning prediction model of prolonged delay to loop ileostomy closure after rectal cancer surgery: a retrospective study
The XGBoost machine learning model we constructed showed good performance in predicting delayed closure of loop ileostomy in rectal cancer patients. In addition, the SHAP method can help better understand the results of machine learning models.
Authors
- Wenhao Zhou (ORCID: https://orcid.org/0000-0001-8956-7238)
- Yonglan Ge
- Mengxiao Jiang (ORCID: https://orcid.org/0009-0006-0509-0019)
- Meichun Zheng
- Zhichao Zheng (ORCID: https://orcid.org/0000-0003-3064-976X)
- Yang Xia (ORCID: https://orcid.org/0000-0003-2487-2244)
- Xiaoping Chen (ORCID: https://orcid.org/0000-0001-8636-0493)
- Baojia Luo (ORCID: https://orcid.org/0009-0009-5674-0853)
- Huiting Zhang (ORCID: https://orcid.org/0000-0002-2417-6874)
- Jianying Liu (ORCID: https://orcid.org/0000-0001-7741-7339)
Institutions
- Sun Yat-sen University (CN)
- Sun Yat-sen University Cancer Center (CN)
Publication Details
- Journal
- World Journal of Surgical Oncology
- Published
- 2025-05-14
- DOI
- https://doi.org/10.1186/s12957-025-03843-w
- Citations
- 1
- Primary Topic
- Colorectal Cancer Surgical Treatments
- Type
- article
- Field-Weighted Citation Impact
- 1.05