Identification of key factors and explainability analysis for surgical decision-making in hepatic alveolar echinococcosis assisted by machine learning
Vascular invasion is the dominant factor influencing the choice of surgical approach in HAE. Machine-learning models, particularly XGBoost, can provide transparent and data-driven support for personalized decision-making.
Authors
- Lei Yin
- Renyong Lin (ORCID: https://orcid.org/0000-0001-6210-4719)
- Tuerganaili Aji (ORCID: https://orcid.org/0000-0001-6737-8874)
- Zhongdian Yuan
- Rexiati Ruze (ORCID: https://orcid.org/0000-0002-0891-5307)
- Alimu Tulahong
- Tiemin Jiang (ORCID: https://orcid.org/0000-0001-6582-5773)
- Dalong Zhu (ORCID: https://orcid.org/0000-0002-1064-1261)
- Chang Liu (ORCID: https://orcid.org/0000-0002-1232-8308)
- Yingmei Shao (ORCID: https://orcid.org/0000-0001-5154-345X)
- Ayinuer Aierken
- Wei Tan
Institutions
- Xinjiang Medical University (CN)
- First Affiliated Hospital of Xinjiang Medical University (CN)
Publication Details
- Journal
- World Journal of Gastroenterology
- Published
- 2025-09-25
- DOI
- https://doi.org/10.3748/wjg.v31.i37.111038
- Citations
- 2
- Primary Topic
- Public Health and Nutrition
- Type
- article
- Field-Weighted Citation Impact
- 6.99