Using clinical and radiographic variables to predict intracranial aneurysm rupture status with machine learning.

ABSTRACT: With excitement in the medical community around artificial intelligence, machine learning (ML) techniques have been applied to correlate clinical and radiographic variables with intracranial aneurysm (IA) rupture status. In this study, we applied various ML techniques, including random forest (RF), XGBoost (XGB), support vector machines (SVM), and multi-layer perceptron (MLP), to predict IA rupture status. The dataset consisted of 178 IAs each with 53 clinical and radiographic features for analysis. We removed features with high correlation (>0.8) with respect to the target variable to reduce redundancy. We applied grid search to fine-tune the hyperparameters for each model. Each model was evaluated across five iterations of 5-fold cross-validation. Overall performance metrics (accuracy, p

SUBMITTER: Johnson MD 

PROVIDER: S-EPMC12361647 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature