ORIGINAL RESEARCH article
Front. Physiol.
Sec. Computational Physiology and Medicine
Volume 16 - 2025 | doi: 10.3389/fphys.2025.1675853
Salp Swarm-Optimized Machine Learning Models for Predicting Preoperative Aortic Rupture Risk in Acute Type A Aortic Dissection Patients
Provisionally accepted- 1National Institute for Data Science in Health and Medicine, School of Medicine, Xiamen University, Xiamen, China
- 2Department of intensive care unit, Xiamen University Xiamen Cardiovascular Hospital, Xiamen, China
- 3School of Informatics,, Xiamen University, Xiamen, China
- 4Department of Cardiac Surgery, Xiamen University Xiamen Cardiovascular Hospital, Xiamen, China
- 5Department of cardiac rehabilitation, Xiamen University Xiamen Cardiovascular Hospital, Xiamen, China
- 6School of Informatics, Xiamen University, Xiamen, China
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Acute Type A aortic dissection (ATAAD) is characterized by acute onset and rapid progression, with aortic rupture due to dissection extension being the primary lethal mechanism. Timely identification of high-risk patients is critical for prioritizing surgical intervention to reduce rupture incidence. This study aimed to develop and validate an interpretable machine learning model to predict aortic rupture in ATAAD patients, thereby improving risk classification and supporting clinical decisions. Medical records of ATAAD patients from Xiamen Cardiovascular Hospital (January 2019–October 2024) were retrospectively analyzed. Predictors were screened via statistical significance (p<0.05) using seven machine learning algorithms, with the Salp Swarm Optimization Algorithm(SSA) optimizing hyperparameters for Random Forest and XGBoost models. To address class imbalance (47 rupture cases, 6.1%), SMOTE was implemented for data augmentation. Model performance was evaluated by accuracy, F1-score, precision, ROC-AUC, sensitivity, and specificity, supplemented by interpretability analyses through feature importance ranking and SHAP. Among 774 included ATAAD patients, the SSA-optimized Random Forest model achieved optimal performance (test dataset: 97.41% accuracy, 0.980 ROC-AUC, 81.82% F1-score). Key predictors included estimated glomerular filtration rate (eGFR), hypotension at admission, and white blood cell count. This work provides a quantitative tool for emergency care prioritization, with SSA enhancing model precision for high-risk patient identification, though multicenter studies are needed to validate generalizability.
Keywords: Type a aortic dissection, Rupture risk prediction, Clinical decision support, Salp Swarm OptimizationAlgorithm, machine learning
Received: 29 Jul 2025; Accepted: 09 Oct 2025.
Copyright: © 2025 Bao, Sun, Cui, Cai, Zheng, Peng and Yang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Weiliang Zheng, 175711337@qq.com
Hua Peng, penghua86@126.com
Chenhui Yang, chyang@xmu.edu.cn
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