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

Table 1 Participant demographics

	Depression group	Non-depression group	P value	OR (95%CI)
Number	543	228
Age (years), mean ± SD	15.20 ± 1.69	15.30 ± 1.67	0.460
Gender
Male	126 (23.20)	119 (52.19)	< 0.001	3.61 (2.60-5.01)
Female	417 (76.80)	109 (47.81)
Education
Junior high school	242 (44.57)	148 (64.91)	< 0.001	2.30 (1.67-3.17)
High school	301 (55.43)	80 (35.09)

Data are presented as n (%). OR: Odds ratio; CI: Confidence intervals; SD: Standard deviation.

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.

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.

Table 4 Statistical comparison between multimodal and bimodal extreme gradient boosting models

	Multimodal	Bimodal	t value	P value	Cohen’s d	Effect size
AUC-ROC	0.99 ± 0.01	0.96 ± 0.04	4.52	0.00	1.17	Large
AUC-PR	0.99 ± 0.00	0.98 ± 0.03	3.87	0.00	1.04	Large
Accuracy	0.95 ± 0.03	0.93 ± 0.03	2.95	0.01	0.76	Moderate-to-large
Precision	0.96 ± 0.03	0.94 ± 0.04	2.18	0.03	0.56	Moderate
Recall	0.97 ± 0.02	0.96 ± 0.03	1.51	0.14	0.39	Small-to-moderate
F1 score	0.97 ± 0.02	0.95 ± 0.02	2.95	0.01	0.74	Moderate-to-large

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

Table 5 Performance stability: Multimodal vs bimodal extreme gradient boosting models

	Levene’s test P value	Interpretation
AUC-ROC	0.00	Significant difference
AUC-PR	0.00	Significant difference
Accuracy	0.39	No significant difference
Precision	0.55	No significant difference
Recall	0.04	Significant difference
F1 score	0.49	No significant difference

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