Table 2.
Performance of single models and the FMDLS
| RM | CM | FMDLS | |||||||
|---|---|---|---|---|---|---|---|---|---|
| MAE | Accuracy (95% CI) | Specificity (95% CI) | Sensitivity (95% CI) | AUC (95% CI) | F1-score | MAE | r | Performance improvement | |
| Spherea | 0.86 | 0.790 (0.751–0.842) | 0.991 (0.974–0.998) | 0.795 (0.745–0.839) | 0.798 (0.748–0.842) | 0.775 | 0.63 | 0.815 | 27.10% |
| Sphereb | 0.66 | 0.850 (0.825–0.875) | 0.996 (0.99–0.998) | 0.859 (0.835–0.883) | 0.863 (0.839–0.887) | 0.828 | 0.50 | 0.949 | 29.41% |
| Cylinder | 0.38 | 0.860 (0.836–0.884) | 0.989 (0.982–0.996) | 0.861 (0.837–0.885) | 0.834 (0.808–0.860) | 0.863 | 0.31 | 0.807 | 26.67% |
| Axis | – | 0.890 (0.816–0.964) | 0.941 (0.849–0.981) | 0.882 (0.776–0.944) | 0.814 (0.708–0.902) | 0.880 | – | – | – |
RM regression model, CM classification model, FMDLS fusion model-based deep learning system, MAE mean absolute error, AUC area under the curve
aModel without age as an eigenvector
bModel with age as an eigenvector