Table 2. Machine learning classification results (1st run) in motor activity time series from depressed patients (n = 23) and healthy controls (n = 32).

Machine Learning Approach	Class Balancing Technique	Classification results by label
		Sensitivity	Specificity	Weighted Recall	PPV	NPV	Weighted Precision	Accuracy	MCC	TP	TN	FP	FN
	Baseline	0.22	0.75	0.53	0.38	0.57	0.49	0.53	−0.4	5	24	8	18
Random Forest	No oversampling	0.44	0.75	0.62	0.56	0.65	0.61	0.62	0.19	10	24	8	13
	Random oversampling	0.52	0.75	0.65	0.60	0.69	0.65	0.65	0.28	12	24	8	11
	SMOTE	0.61	0.75	0.69	0.64	0.73	0.69	0.69	0.36	14	24	8	9
	Baseline	0.22	0.88	0.60	0.56	0.61	0.59	0.60	0.12	5	28	4	18
Deep	No oversampling	0.43	0.84	0.67	0.67	0.68	0.67	0.67	0.31	10	27	5	13
Neural Network	Random oversampling	0.52	0.81	0.69	0.67	0.70	0.69	0.69	0.35	12	26	6	11
	SMOTE	0.57	0.75	0.67	0.62	0.71	0.67	0.67	0.32	13	24	8	10
Weighted Deep	Baseline	0.22	0.88	0.60	0.56	0.61	0.59	0.60	0.12	5	28	4	18
Neural Network	No oversampling	0.61	0.69	0.65	0.58	0.71	0.66	0.65	0.29	14	22	10	9
Weighted Convolutional	Baseline	0.35	0.59	0.49	0.38	0.56	0.48	0.49	−0.06	8	19	13	15
Neural Network	No oversampling	0.65	0.78	0.73	0.68	0.76	0.73	0.73	0.44	15	25	7	8

TP: True Positives (condition cases classified correctly as labeled).

FN: False Negatives (condition cases misclassified as control cases).

TN: True Negatives (control cases classified correctly as labeled).

FP: False Positives (controls cases misclassified as condition cases).

Sensitivity: True Positive Rate; TP / (TP + FN). Specificity: True Negative Rate; TN / (TN + FP). Weighted Recall: (Sensitivity x (TP + FN)) + (Specificity x (TN + FP)) / (TP + FN + TN + FP). PPV: Positive Predictive Value; TP / (TP + FP). NPV: Negative Predictive Value: TN / (TN + FN). Weighted Precision: (PPV x (TP + FN)) + (NPV x (TN + FP)) / (TP + FN + TN + FP). Accuracy: (TP + TN) / (TP + TN + FP + FN). MCC: Matthews Correlation Coefficient: ((TP x TN)–(FP x FN)) / sqrt ((TP + FP) x (TP + FN) x (TN + FP) x (TN + FN)).