Using fractal dimension to predict the risk of intra cranial aneurysm rupture with machine learning
This work addresses a critical medical problem for patients with intracranial aneurysms, offering a potential improvement over traditional risk models like PHASES, though it appears incremental as it applies existing methods to new data.
The study tackled the problem of predicting intracranial aneurysm rupture risk by comparing four machine learning algorithms on clinical and radiographic features, with Random Forest achieving the highest accuracy of 85% and fractal dimension identified as the most important feature.
Intracranial aneurysms (IAs) that rupture result in significant morbidity and mortality. While traditional risk models such as the PHASES score are useful in clinical decision making, machine learning (ML) models offer the potential to provide more accuracy. In this study, we compared the performance of four different machine learning algorithms Random Forest (RF), XGBoost (XGB), Support Vector Machine (SVM), and Multi Layer Perceptron (MLP) on clinical and radiographic features to predict rupture status of intracranial aneurysms. Among the models, RF achieved the highest accuracy (85%) with balanced precision and recall, while MLP had the lowest overall performance (accuracy of 63%). Fractal dimension ranked as the most important feature for model performance across all models.