Bridging the gap between subjective and objective measures: A multimodal protocol for adolescent depression detection

Table 2 Comparative performance of multimodal models, mean ± SD

	Accuracy	Precision	Recall	F1 score	AUC-ROC	AUC-PR
XGBoost	0.95 ± 0.03	0.96 ± 0.03	0.97 ± 0.02a	0.97 ± 0.02a	0.99 ± 0.01	1.00 ± 0.00
Random forest	0.94 ± 0.03	0.95 ± 0.03	0.96 ± 0.03	0.95 ± 0.02	0.98 ± 0.01	0.99 ± 0.01
Logistic regression	0.94 ± 0.03	0.96 ± 0.03	0.96 ± 0.03	0.96 ± 0.02	0.98 ± 0.01	0.99 ± 0.01
Support vector machine	0.94 ± 0.03	0.96 ± 0.03	0.95 ± 0.03	0.95 ± 0.02	0.98 ± 0.01	0.99 ± 0.01
Artificial neural networks	0.74 ± 0.03c	0.73 ± 0.02c	1.00 ± 0.00c	0.85 ± 0.01c	0.78 ± 0.08c	0.85 ± 0.06c

^aP < 0.05.

^cP < 0.001.

XGBoost: Extreme gradient boosting; AUC-ROC: Under the receiver operating characteristic curve; AUC-PR: Area under the precision-recall curve.