Predictions of Small Intracranial Aneurysms’ Rupture Risk with Ensemble Machine Learning Model (Super Learner): A Retrospective Study in Two Tertiary Hospitals in China

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 workCorrespondence: Xiaoyu Tang, Department of neurosurgery, Suzhou municipal hospital affiliated to Nanjing medical university, Suzhou, Jiangsu Province, People’s Republic of China, Email [email protected] Yibin Fang, Tongji University School of Medicine, Tongji University Affiliated Shanghai 4th People’s Hospital, Shanghai, People’s Republic of China, Email [email protected]: 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