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已发表论文
基于集成机器学习模型（超级学习器）预测颅内小动脉瘤破裂风险：中国两家三级医院的回顾性研究
Authors Hu X , Ye S, Qi D, Li S, Tang X, Fang Y
Received 21 May 2025
Accepted for publication 20 August 2025
Published 3 November 2025 Volume 2025:18 Pages 6637—6649
DOI https://doi.org/10.2147/IJGM.S533558
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Woon-Man Kung
Xiaolong Hu,1,2,* Shifei Ye,2,* Dayong Qi,2 Suya Li,2 Xiaoyu Tang,3 Yibin Fang1,2,4
1Tongji University School of Medicine, Tongji University Affiliated Shanghai 4th People’s Hospital, Shanghai, People’s Republic of China; 2Department of Neurovascular Disease, Tongji University Affiliated Shanghai 4th People’s Hospital, Shanghai, People’s Republic of China; 3Department of Neurosurgery, Suzhou Municipal hospital affiliated to Nanjing Medical university, Suzhou, Jiangsu Province, People’s Republic of China; 4Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Xiaoyu Tang, Department of neurosurgery, Suzhou municipal hospital affiliated to Nanjing medical university, Suzhou, Jiangsu Province, People’s Republic of China, Email xiaoyutang@njmu.edu.cn Yibin Fang, Tongji University School of Medicine, Tongji University Affiliated Shanghai 4th People’s Hospital, Shanghai, People’s Republic of China, Email fangyibin@tongji.edu.cn
Purpose: This research aims to investigate the morphological, clinical and hemodynamic parameters influencing intracranial aneurysm rupture, develop a ensemble machine learning model (Super Learner) to predict its rupture risk.
Methods: This retrospective study analyzed aneurysm patients from two hospitals. Five base learners—decision tree (DT), k-nearest neighbor (KNN), random forest (RF), support vector machine (SVM) and extreme gradient boosting (XGBoost)—were constructed based on demographic, hemodynamic and geometric parameters. A Super Learner model was subsequently constructed using ensemble learning algorithms, with all models validated on an independent external dataset.
Results: The dataset comprised 97 patients in the training cohort, 42 in the internal validation cohort, and 86 in the external validation cohort. In the external validation cohort, the area under the curve (AUC) values: Super learner 0.94 (0.89– 1.00), Random Forest 0.83 (0.76– 0.91), XGBoost 0.93 (0.87– 0.99), Support Vector Machine 0.82 (0.73– 0.92), Decision Tree 0.84 (0.76– 0.93), and k-Nearest Neighbors 0.51 (0.38– 0.63).
Conclusion: The Super Learner model outperforms individual models in both performance and stability for predicting intracranial aneurysm rupture risk. It has been robustly validated on an external dataset, demonstrating strong generalizability.
Keywords: super learner, machine learning, aneurysm, predictive model
1Tongji University School of Medicine, Tongji University Affiliated Shanghai 4th People’s Hospital, Shanghai, People’s Republic of China; 2Department of Neurovascular Disease, Tongji University Affiliated Shanghai 4th People’s Hospital, Shanghai, People’s Republic of China; 3Department of Neurosurgery, Suzhou Municipal hospital affiliated to Nanjing Medical university, Suzhou, Jiangsu Province, People’s Republic of China; 4Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Xiaoyu Tang, Department of neurosurgery, Suzhou municipal hospital affiliated to Nanjing medical university, Suzhou, Jiangsu Province, People’s Republic of China, Email xiaoyutang@njmu.edu.cn Yibin Fang, Tongji University School of Medicine, Tongji University Affiliated Shanghai 4th People’s Hospital, Shanghai, People’s Republic of China, Email fangyibin@tongji.edu.cn
Purpose: This research aims to investigate the morphological, clinical and hemodynamic parameters influencing intracranial aneurysm rupture, develop a ensemble machine learning model (Super Learner) to predict its rupture risk.
Methods: This retrospective study analyzed aneurysm patients from two hospitals. Five base learners—decision tree (DT), k-nearest neighbor (KNN), random forest (RF), support vector machine (SVM) and extreme gradient boosting (XGBoost)—were constructed based on demographic, hemodynamic and geometric parameters. A Super Learner model was subsequently constructed using ensemble learning algorithms, with all models validated on an independent external dataset.
Results: The dataset comprised 97 patients in the training cohort, 42 in the internal validation cohort, and 86 in the external validation cohort. In the external validation cohort, the area under the curve (AUC) values: Super learner 0.94 (0.89– 1.00), Random Forest 0.83 (0.76– 0.91), XGBoost 0.93 (0.87– 0.99), Support Vector Machine 0.82 (0.73– 0.92), Decision Tree 0.84 (0.76– 0.93), and k-Nearest Neighbors 0.51 (0.38– 0.63).
Conclusion: The Super Learner model outperforms individual models in both performance and stability for predicting intracranial aneurysm rupture risk. It has been robustly validated on an external dataset, demonstrating strong generalizability.
Keywords: super learner, machine learning, aneurysm, predictive model