Rupture Risk Assessment for Cerebral Aneurysm Using Interpretable Machine Learning on Multidimensional Data - Ou, Chubin; Liu, Jiahui; Qian, Yi; Chong, Winston; Zhang, Xin; Liu, Wenchao; Su, Hengxian; Zhang, Nan; Zhang, Jianbo; Duan, Chuan-Zhi; He, Xuying (Frontiers in Neurology, 2020)
Background: Assessment of cerebral aneurysm rupture risk is an important task but it remains challenging. Recent works applying machine learning to rupture risk evaluation presented positive results. Yet they were based on limited aspects of data and lack of interpretability may limit their use in clinical setting. We aimed to develop interpretable machine learning models on multidimensional data for aneurysm rupture risk assessment. Materials and Methods: 374 aneurysms were included in the study. Demographic, medical history, lifestyle behaviors, lipid profile and morphologies were collected for each patient. Prediction models were derived using machine learning methods (support vector machine, artificial neural network and XGBoost) and conventional logistic regression. The derived models were compared with the PHASES score method. The Shapley Additive Explanations (SHAP) analysis was applied to improve the interpretability of the best machine learning model and reveal the reasoning behind the predictions made by the model. Results: The best machine learning model (XGBoost) achieved an area under the receiver operating characteristic curve of 0.882 (95% CI 0.838-0.927), significantly better than the logistic regression model (0.779 95% CI 0.729-0.829, P<0.05) and the PHASES score method (0.758 95% CI 0.713-0.800, P<0.05). Location, size ratio and triglyceride level were the three most important features in predicting rupture. Two typical cases were analyzed to demonstrate the interpretability of the model. Conclusions: This study demonstrated the potential of using machine learning for aneurysm rupture risk assessment. Machine learning models performed better than conventional statistical model and the PHASES score method. The SHAP analysis can improve the interpretability of machine learning models and facilitate their use in a clinical setting.