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Methods: Three hundred seventy-four 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% confidence interval (CI) = 0.838–0.927], significantly better than the logistic regression model (0.779; 95% CI = 0.729–0.829; P = 0.002) and the PHASES score method (0.758; 95% CI = 0.713–0.800; P = 0.001). 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.
Bibliographical noteCopyright the Author(s) 2020. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.
- intracranial aneurysm
- machine learning
- subarachnoid hemorrhage
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- 1 Active
NHMRC - PG: Advanced computational fluid dynamic (CFD) technology to predict cerebrovascular aneurysm haemorrhage
Sen, I., Morgan, M., Mao, Y., Di Ieva, A., Javadzadegan, A., Yang, Q. & Zhu, F.
1/01/19 → 31/12/23