In the field of surgery treatment, thoracic endovascular aortic repair has recently gained popularity, but this treatment often causes an adverse clinical side effect called endoleak. The risk prediction of endoleak is essential for preoperative planning (Nakatamari et al., J Vasc Interv Radiol 22(7):974–979, 2011). In this study, we focus on a quantitative curvature in the morphology of a patient’s aorta, and predict the risk of endoleak formation through linear discriminant analysis. Here, we objectively evaluate the relationship between the side effect after stent-graft treatment for thoracic aneurysm and a patient’s native thoracic aortic curvature. In addition, based on the sample influence function for the average of discriminant scores in linear discriminant analysis, we also perform statistical diagnostics on the result of the analysis. We detected the influential training samples to be deleted to realize improved prediction accuracy, and made subsets of all of their possible combinations. Furthermore, by considering the minimum misclassification rate based on leave-one-out cross-validation in Hastie et al. (The elements of statistical learning. Springer, New York, 2001, pp. 214–216) and the minimum number of training samples to be deleted, we deduced the subset to be excluded from training data when we develop the target classifier. From this study, we detected an important part of the native thoracic aorta in terms of risk prediction of endoleak occurrence, and identified influential patients for the result of the discrimination.