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

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.