Interpretable prediction of stroke prognosis: SHAP for SVM and nomogram for logistic regression
This study presents a robust ML approach for predicting stroke prognosis, with the SVM model and Nomogram providing valuable tools for clinical decision-making. By incorporating advanced ML techniques, we enhance the precision of prognostic assessments and offer a theoretical and practical framework for clinical application.
Authors
- Lei Zha (ORCID: https://orcid.org/0000-0003-3294-2513)
- K.J. Chen
- Zhiqin Liu (ORCID: https://orcid.org/0000-0003-2328-4897)
- Kun Guo (ORCID: https://orcid.org/0000-0002-4787-8406)
- Naibing Gu
- Bo Zhu
- Yuan Shao
Institutions
- Xian Central Hospital (CN)
- First Hospital of Xi'an (CN)
Publication Details
- Journal
- Frontiers in Neurology
- Published
- 2025-03-04
- DOI
- https://doi.org/10.3389/fneur.2025.1522868
- Citations
- 8
- Primary Topic
- Acute Ischemic Stroke Management
- Type
- article
- Field-Weighted Citation Impact
- 10.83