Table 1.
Classification metrics of SVM
| Performance metrics | The classification metrics of SVM across three data sets | ||
|---|---|---|---|
| Training set (LOOCV, n = 202) | Internal validation set (n = 120) | External validation set (n = 101) | |
| Accuracy (95% CI) | 0.876 (0.823–0.918) | 0.875 (0.802–0.928) | 0.861 (0.778–0.922) |
| Sensitivity (95% CI) | 0.975 (0.913–0.997) | 0.885 (0.766–0.956) | 0.761 (0.612–0.874) |
| Specificity (95% CI) | 0.811 (0.731–0.877) | 0.868 (0.764–0.938) | 0.945 (0.849–0.989) |
| Positive predicted value | 0.772 (0.678–0.850) | 0.836 (0.712–0.922) | 0.921 (0.786–0.983) |
| Negative predicted value | 0.980 (0.930–0.998) | 0.908 (0.810–0.965) | 0.825 (0.709–0.909) |
| Kappaa | 0.752 | 0.747 | 0.717 |
| F1a | 0.862 | 0.860 | 0.833 |
aKappa measured the agreement between the predicted classification with true labels. F1 was the harmonic average of precision (positive predicted value) and recall rates (sensitivity)