Table 4.

Performance metrics for all machine learning models.

Model	F1 Score	Specificity	Sensitivity	AUC
Logistic	0.64 ± 0.13	0.65 ± 0.14	0.48 ± 0.25	0.55 ± 0.17
BiLSTM	0.70 ± 0.10	0.71 ± 0.20	0.57 ± 0.30	0.70 ± 0.14
TCN	0.73 ± 0.11	0.72 ± 0.14	0.74 ± 0.18	0.74 ± 0.11
CNN-LSTM	0.72 ± 0.11	0.65 ± 0.17	0.82 ± 0.12	0.76 ± 0.12
LSTM-FCN	0.70 ± 0.08	0.71 ± 0.16	0.69 ± 0.14	0.72 ± 0.15
CTA-TCNa	0.80 ± 0.10	0.77 ± 0.14	0.80 ± 0.17	0.77 ± 0.10

AUC Area under the curve, BiLSTM Bidirectional Long Short-Term Memory, TCN Temporal Convolutional Network, CNN-LSTM Convolutional Neural Network Long Short-Term Memory, LSTM-FCN Long Short-Term Memory with Fully Convolutional Network, TCA-CTN Channel-Temporal Attention-Temporal Convolutional Network.

^aDenotes best performing model.