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	–		–		.21a		–		.15b

Note:—CI indicates confidence interval; LR, logistic regression; SVM, support vector machine; RF, random forest; ROC, receiver operation characteristic; RF, random forest; -, NA.

^aP < . 05 means a significant difference exists in AUCs of SVM in the internal and external validation datasets.

^bP < . 05 means a significant difference exists in AUCs of SVM in Taizhou and Tianjin sets.