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

Table 3 Comparative performance of bimodal models

	Accuracy	Precision	Recall	F1 score	AUC-ROC	AUC-PR
XGBoost	0.93 ± 0.03c	0.94 ± 0.04c	0.96 ± 0.03	0.95 ± 0.02a	0.96 ± 0.04a	0.98 ± 0.03a
Random forest	0.91 ± 0.04	0.92 ± 0.05	0.97 ± 0.03	0.94 ± 0.03	0.96 ± 0.03	0.98 ± 0.02
Logistic regression	0.90 ± 0.04	0.91 ± 0.04	0.95 ± 0.04	0.93 ± 0.03	0.95 ± 0.03	0.97 ± 0.02
Support vector machine	0.89 ± 0.04	0.88 ± 0.04	0.99 ± 0.01	0.93 ± 0.02	0.95 ± 0.04	0.97 ± 0.03
Artificial neural networks	0.71 ± 0.00c	0.71 ± 0.04c	1.00 ± 0.00c	0.83 ± 0.00c	0.83 ± 0.06c	0.92 ± 0.03c

^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.