用分形维数结合机器学习预测脑动脉瘤破裂风险
Using fractal dimension to predict the risk of intra cranial aneurysm rupture with machine learning
- 引入分形维数作为关键特征，提升模型对动脉瘤破裂的判断能力
- 随机森林模型准确率达85%，显著优于其他三种算法
- 适合神经介入医生和影像科医师参考临床决策
颅内动脉瘤（IAs）破裂可导致严重残疾和死亡。尽管传统风险评分如PHASES量表在临床决策中有所应用，但机器学习（ML）模型具备更高的预测潜力。本研究比较了四种机器学习算法——随机森林（RF）、XGBoost（XGB）、支持向量机（SVM）和多层感知机（MLP）——在临床与影像学特征上的表现，用于预测颅内动脉瘤的破裂状态。结果显示，随机森林模型达到最高准确率（85%），且精确率与召回率平衡；而多层感知机表现最差，准确率为63%。在所有模型中，分形维数均被识别为最重要的特征。
原文摘要 · Abstract (English)
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.
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