Abdominal aortic aneurysm refers to the irreversible abnormal dilation of the aorta at the abdominal level, and it is acknowledged as one of the leading causes of mortality on a global scale. Most abdominal aortic aneurysms are asymptomatic until they approach the point of rupture; thus, it is essential to establish an efficient workflow for the accurate detection of this condition to enhance clinical outcomes. The incorporation of artificial intelligence learning algorithms into healthcare workflows holds the prospect of significantly improving the accuracy of decision-making related to patient mortality risk. Since the potential surgical repair of an aortic aneurysm depends upon the maximum external diameter of the aneurysm, this study aims to develop an end-to-end algorithmic method for classifying low-risk and high-risk cases based on abdominal aortic aneurysm data. To perform the predictive analysis, we adopt neuro-fuzzy systems, ensembles of neuro-fuzzy systems, and hybrid evolutionary-based fuzzy classifiers. The models are trained using features extracted from the radiomics framework and exhibit high generalisation performance, as measured by the adopted metrics, and estimated on a K-fold cross-validation basis. Numerical studies further reveal that the hybrid evolutionary-based fuzzy system exhibits exceptional accuracy in distinguishing between the two identified classes.