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Performance of SVM to predict rupture status of small aneurysms in the training, internal validation, and external validation datasets
Training Set (n = 410) Internal Validation Set (n = 94) External Validation Set (n = 52) Tianjin Set (n = 30) Taizhou Set (n = 22)
AUC 0.88 0.91 0.82 0.71 0.90
95% CI 0.85–0.92 0.74–0.98 0.69–0.94 0.52–0.86 0.70–0.99
Sensitivity 73.4% 77.3% 68.2% 54.5% 81.8%
Specificity 91.1% 84.2% 76.7% 73.7% 81.8%
Delong test – – .21^{^a} – .15^{^b}
Note:—CI indicates confidence interval; LR, logistic regression; SVM, support vector machine; RF, random forest; ROC, receiver operation characteristic; RF, random forest; -, NA.
↵a P < . 05 means a significant difference exists in AUCs of SVM in the internal and external validation datasets.
↵b P < . 05 means a significant difference exists in AUCs of SVM in Taizhou and Tianjin sets.