Using fractal dimension to predict the risk of intra cranial aneurysm rupture with machine learning

TL;DR

This study evaluates machine learning models to predict intracranial aneurysm rupture risk, highlighting fractal dimension as a key feature, with Random Forest achieving the highest accuracy among tested algorithms.

Contribution

The paper demonstrates the effectiveness of machine learning models, especially Random Forest, in predicting aneurysm rupture risk using radiographic features, emphasizing the importance of fractal dimension.

Findings

Random Forest achieved 85% accuracy.

Fractal dimension was the most important feature.

ML models outperformed traditional risk scores.

Abstract

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.