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.
↵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.