Cerebral aneurysm rupture status classification using statistical and machine learning methods

Morphological characterization and fluid dynamics simulations were carried out to classify the rupture status of 71 (36 unruptured, 35 ruptured) patient specific cerebral aneurysms using a machine learning approach together with statistical techniques. Eleven morphological and six hemodynamic parameters were evaluated individually and collectively for significance as rupture status predictors. The performance of each parameter was inspected using hypothesis testing, accuracy, confusion matrix, and the area under the receiver operating characteristic curve. Overall, the size ratio exhibited the best performance, followed by the diastolic wall shear stress, and systolic wall shear stress. The prediction capability of all 17 parameters together was evaluated using eight different machine learning algorithms. The logistic regression achieved the highest accuracy (0.75), whereas the random forest had the highest area under curve value among all the classifiers (0.82), surpassing the performance exhibited by the size ratio. Hence, we propose the random forest model as a tool that can help improve the rupture status prediction of cerebral aneurysms.

Automated summary

Morphological characterization and fluid dynamics simulations were used to classify ruptured and unruptured cerebral aneurysms in 71 patients, with size ratio, diastolic wall shear stress, and systolic wall shear stress showing the best predictive performance. Machine learning algorithms were employed, with logistic regression achieving the highest accuracy and random forest exhibiting the highest area under curve value, surpassing the performance of individual parameters like size ratio. The random forest model is proposed as a tool to improve rupture status prediction of cerebral aneurysms.