Table 3 Overall performance of schizophrenic speech detection using Sch-net and its components (backbone, skip connection (SC), and CBAM)
From: Sch-net: a deep learning architecture for automatic detection of schizophrenia
Evaluated indicators | 95% CI | |||
|---|---|---|---|---|
Backbone | Backbone + SC | Backbone + CBAM | Sch-net (ours) | |
Accuracy | 0.9323 | 0.9494 | 0.9563 | 0.9768 |
(0.9295,0.9351) | (0.9460,0.9528) | (0.9534,0.9591) | (0.9739,0.9797) | |
Precision | 0.9480 | 0.9634 | 0.9513 | 0.9639 |
(0.9445,0.9515) | (0.9564,0.9704) | (0.9458,0.9568) | (0.9585,0.9693) | |
Recall | 0.9149 | 0.9348 | 0.9622 | 0.9908 |
(0.9100,0.9197) | (0.9326,0.9370) | (0.9556,0.9688) | (0.9898,0.9918) | |
F1-score | 0.9311 | 0.9487 | 0.9565 | 0.9771 |
(0.9280,0.9341) | (0.9456,0.9519) | (0.9536,0.9594) | (0.9743,0.9799) | |
Sensitivity | 0.9176 | 0.9619 | 0.9902 | 0.9914 |
(0.9131,0.9221) | (0.9581,0.9657) | (0.9847,0.9956) | (0.9863,0.9964) | |
Specificity | 0.9488 | 0.9601 | 0.9494 | 0.9738 |
(0.9415,0.9561) | (0.9513,0.9689) | (0.9437,0.9551) | (0.9656,0.9820) | |
AUC | 0.9593 | 0.9892 | 0.9902 | 0.9978 |
(0.9577,0.9609) | (0.9859,0.9924) | (0.9880,0.9924) | (0.9965,0.9990) | |