Interpretable prognostic modeling for long-term survival of Type A aortic dissection patients using support vector machine algorithm

Interpretable prognostic modeling for long-term survival of Type A aortic dissection patients using support vector machine algorithm

Abstract Objectives This study aims to develop a reliable and interpretable predictive model for long-term survival in Type A aortic dissection (TAAD) patients, utilizing machine learning (ML) algorithms. Methods We retrospectively reviewed the clinical data of patients diagnosed with TAAD who under...

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Bibliographic Details
Main Authors: Hao Cai, Yue Shao, Xuan-yu Liu, Chang-ying Li, Hao-yu Ran, Hao-ming Shi, Cheng Zhang, Qing-chen Wu
Format: Article
Language:English
Published: BMC 2025-04-01
Series:European Journal of Medical Research
Subjects:
Type A aortic dissection
Machine learning
Long-term survival
Predictive model
Support vector machine (SVM)
Online Access:https://doi.org/10.1186/s40001-025-02510-w
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https://doi.org/10.1186/s40001-025-02510-w

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