This study aims to review retrospectively the records of Asian patients diagnosed with abdominal aortic aneurysm to investigate the potential correlations between clinical and morphological parameters within the context of whether the aneurysms were ruptured or unruptured. A machine-learning-based approach is proposed to predict the rupture status of Asian abdominal aortic aneurysm by comparing four different classifiers trained with clinical and geometrical parameters obtained from computed tomography images. The classifiers were applied on 312 patient data sets obtained from a regulatory-approved database. The data sets included 17 attributes under three classes: unruptured abdominal aortic aneurysm, ruptured abdominal aortic aneurysm, and normal aorta without aneurysm. Four different classification models, namely, Decision trees, Naïve Bayes, logistic regression, and support vector machines were applied to the patient data set. The models were evaluated by 10-fold cross-validation and the classifier performances were assessed with classification accuracy, area under the curve of receiver operator characteristic, and F-measures. Data analysis and evaluation were performed using the Weka machine learning application. The results indicated that Naïve Bayes achieved the best performance among the classifiers with a classification accuracy of 95.2%, an area under the curve of 0.974, and an F-measure of 0.952. The clinical implications of this work can be addressed in two ways. The best classifier can be applied to prospectively acquired data to predict the likelihood of aneurysm rupture. Next, it would be necessary to estimate the attributes implicated in rupture risk beyond just maximum aneurysm diameter.
Keywords: Abdominal aortic aneurysm; geometric attributes; machine learning; rupture risk.