An integrated machine learning framework for cerebral aneurysm rupture status classification using clinical, morphological, and hemodynamic features

Accurate assessment of cerebral aneurysm rupture status remains a critical challenge in neurovascular research due to the complex interplay between geometric, hemodynamic, and clinical factors. In this study, we developed and evaluated a machine-learning framework for retrospective rupture-status classification in 708 patient-specific aneurysms by integrating 55 predictors: three clinical variables, 35 morphological descriptors, and 17 hemodynamic parameters derived from computational fluid dynamics (CFD) simulations. Blood flow was modeled as incompressible laminar flow with a non-Newtonian Carreau–Yasuda viscosity model to capture shear-thinning behavior in cerebral arteries. We compared 13 supervised classifiers, 10 unsupervised clustering approaches, and two neural-network models. Model training and hyperparameter optimization were performed using stratified five-fold cross-validation within a 90/10 training–testing split, with performance evaluated using ROC–AUC, precision, recall, specificity, and F1-score. Among the evaluated models, BaggingSVM achieved the strongest discrimination on the independent test set (ROC–AUC = 0.78), followed by SVM (0.75) and Gaussian Process (0.75). A scenario comparison using BaggingSVM showed that the combined feature set improved classification performance (AUC = 0.78) compared with clinical+morphological features (AUC = 0.76) and clinical+hemodynamical features (AUC = 0.71) settings. Sensitivity-oriented threshold tuning increased recall for ruptured aneurysm detection, reaching values up to 0.96 for BaggingSVM. Permutation feature importance analysis revealed consistent dominance of ellipticity index, low shear area ratio (LSAR), aneurysm location, irregularity and aneurysm angle across models. a small subset of variables contributes disproportionately to rupture-status classification, although the reduced-feature analysis indicates that lower-ranked predictors collectively provide additional discriminatory information. The proposed framework demonstrates the value of integrating physiologically informed hemodynamic modeling with machine learning to improve data-driven rupture status assessment and provides a scalable foundation for future clinical decision-support studies in cerebrovascular diagnostics.