Machine Learning-Based Random Forest to Predict 3-Year Survival after Endovascular Aneurysm Repair
The random forest model effectively predicts 3-year survival after EVAR, indicating key factors such as poor nutritional status, compromised immunity, CKD, octogenarian status, COPD, small aneurysm size, and statin use.
Authors
- Masaki Kano (ORCID: https://orcid.org/0000-0002-3674-9852)
- Masayasu Nishibe
- Shoji Fukuda (ORCID: https://orcid.org/0000-0001-5206-2842)
- Seiji Matsuda (ORCID: https://orcid.org/0000-0002-3159-904X)
- Toshiya Nishibe (ORCID: https://orcid.org/0000-0003-0879-6165)
- Toshiaki IWASA
- Shinobu Akiyama
Institutions
- Tokyo Medical University (JP)
- Hokkaido Information University (JP)
Publication Details
- Journal
- Annals of Thoracic and Cardiovascular Surgery
- Published
- 2025-01-01
- DOI
- https://doi.org/10.5761/atcs.oa.25-00036
- Citations
- 4
- Primary Topic
- Aortic aneurysm repair treatments
- Type
- article
- Field-Weighted Citation Impact
- 6.07