Comparison of statistical learning approaches for cerebral aneurysm rupture assessment
| Autor: | Fernando Mut, Daniel Lückehe, Gabriele von Voigt, Martin Slawski, Philippe Bijlenga, Sven Hirsch, Juan R. Cebral, Felicitas J. Detmer |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Support Vector Machine
Support vector machine Computer science 02 engineering and technology Hemodynamics/physiology 616: Innere Medizin und Krankheiten 030218 nuclear medicine & medical imaging 0302 clinical medicine Decision tree Artificial neural network Shape General Medicine Computer Graphics and Computer-Aided Design Computer Science Applications Random forest Multilayer perceptron Model statistical Radial basis function kernel cardiovascular system Computer Vision and Pattern Recognition Human Intracranial Aneurysm/diagnosis/physiopathology Aneurysm ruptured 0206 medical engineering Biomedical Engineering Health Informatics 006: Spezielle Computerverfahren Aneurysm Ruptured/diagnosis/physiopathology 03 medical and health sciences Machine learning Humans Radiology Nuclear Medicine and imaging cardiovascular diseases Cerebral aneurysm Models Statistical Receiver operating characteristic business.industry Decision Trees Hemodynamics Statistical model Pattern recognition Intracranial aneurysm 020601 biomedical engineering ROC curve ddc:616.8 ROC Curve Surgery Artificial intelligence Risk factor business Prediction |
| Zdroj: | International Journal of Computer Assisted Radiology and Surgery, Vol. 15, No 1 (2020) pp. 141-150 |
| ISSN: | 1861-6410 |
| Popis: | Erworben im Rahmen der Schweizer Nationallizenzen (http://www.nationallizenzen.ch) Purpose: Incidental aneurysms pose a challenge to physicians who need to decide whether or not to treat them. A statistical model could potentially support such treatment decisions. The aim of this study was to compare a previously developed aneurysm rupture logistic regression probability model (LRM) to other machine learning (ML) classifiers for discrimination of aneurysm rupture status. Methods: Hemodynamic, morphological, and patient-related information of 1631 cerebral aneurysms characterized by computational fluid dynamics simulations were used to train support vector machines (SVMs) with linear and RBF kernel (RBF-SVM), k-nearest neighbors (kNN), decision tree, random forest, and multilayer perceptron (MLP) neural network classifiers for predicting the aneurysm rupture status. The classifiers’ accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were evaluated and compared to the LRM using 249 test cases obtained from two external cohorts. Additionally, important variables were determined based on the random forest and weights of the linear SVM. Results: The AUCs of the MLP, LRM, linear SVM, RBF-SVM, kNN, decision tree, and random forest were 0.83, 0.82, 0.80, 0.81, 0.76, 0.70, and 0.79, respectively. The accuracy ranged between 0.76 (decision tree,) and 0.79 (linear SVM, RBF-SVM, and MLP). Important variables for predicting the aneurysm rupture status included aneurysm location, the mean surface curvature, and maximum flow velocity. Conclusion: The performance of the LRM was overall comparable to that of the other ML classifiers, confirming its potential for aneurysm rupture assessment. To further improve the predictions, additional information, e.g., related to the aneurysm wall, might be needed. |
| Databáze: | OpenAIRE |
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