Table 2.
The performance of the single-view model and multi-view fusion model.
| Dataset | Model | AUC* | Accuracy* | Sensitivity* | Specificity* | P value |
|---|---|---|---|---|---|---|
| Training set | Single-view | 0.767 (0.718 - 0.816) |
0.686 (0.685 - 0.687) |
0.663 (0.609 - 0.717) |
0.752 (0.668 - 0.873) |
< 0.001 |
| Multi-view | 0.905 (0.871 - 0.939) |
0.833 (0.832 - 0.834) |
0.823 (0.780 - 0.867) |
0.861 (0.794 - 0.929) |
||
| Validation set | Single-view | 0.642 (0.465 - 0.820) |
0.640 (0.631 - 0.649) |
0.676 (0.525 - 0.827) |
0.538 (0.267 - 0.809) |
0.423 |
| Multi-view | 0.732 (0.569 - 0.895) |
0.700 (0.692 - 0.708) |
0.730 (0.587 - 0.873) |
0.615 (0.351 - 0.880) |
||
| Testing set | Single-view | 0.634 (0.469 - 0.799) |
0.620 (0.611 - 0.629) |
0.622 (0.465 - 0.778) |
0.615 (0.351 - 0.880) |
0.08 |
| Multi-view | 0.819 (0.673 - 0.965) |
0.760 (0.753 - 0.767) |
0.811 (0.685 - 0.937) |
0.615 (0.351 - 0.880) |
Note: Delong test is used to test the differences between the AUC of single-view model and multi-view model.
*
Quantitative data were presented as value (95% confidence interval).