Interpretable prognostic modeling for long-term survival of Type A aortic dissection patients using support vector machine algorithm
This study developed an interpretable predictive model based on the SVM algorithm to assess long-term survival in TAAD patients. The model demonstrated accuracy, precision, and robustness in identifying high-risk patients, providing reliable evidence for clinicians.
Authors
- Changying Li (ORCID: https://orcid.org/0000-0003-2590-4797)
- Qingchen Wu (ORCID: https://orcid.org/0000-0002-7807-8882)
- Haoming Shi (ORCID: https://orcid.org/0000-0002-0228-6116)
- Haoyu Ran
- Hao Cai (ORCID: https://orcid.org/0000-0003-2762-1803)
- Xuanyu Liu (ORCID: https://orcid.org/0000-0001-8916-9942)
- Cheng Zhang (ORCID: https://orcid.org/0000-0002-6604-2380)
- Yue Shao (ORCID: https://orcid.org/0000-0001-7953-5865)
Institutions
- The Affiliated Yongchuan Hospital of Chongqing Medical University (CN)
- Chongqing Medical University (CN)
Publication Details
- Journal
- European journal of medical research
- Published
- 2025-04-14
- DOI
- https://doi.org/10.1186/s40001-025-02510-w
- Citations
- 4
- Primary Topic
- Cardiac Valve Diseases and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 5.55
Funders
- National Natural Science Foundation of China