Table 3.

Baseline classification result. Opt signifies optimizer, Lr signifies learning rate, Adam signifies adaptive moment estimation while SGD signifies stochastic gradient descent, and Lf signifies loss function.

Models	Accuracy (%)	Sensitivity (%)	Specificity (%)	Precision (%)	F1 Score	AUC
Opt: Adam, Lf: Categorical Smooth Loss, Lr: ${10}^{-4}$
Baseline	0.90468	0.81116	0.93631	0.83653	0.81355	0.87373
Opt: Adam, Lf: Categorical Smooth Loss, Lr: ${10}^{-3}$
Baseline	0.9097	0.81579	0.93941	0.83356	0.81979	0.87881
Opt: Adam, Lf: Categorical Cross-Entropy, Lr: ${10}^{-3}$
Baseline	0.91973	0.84466	0.94713	0.85426	0.84114	0.89426
Opt: SGD, Lf: Categorical Cross-Entropy, Lr: ${10}^{-4}$
Baseline	0.90301	0.80312	0.93645	0.81609	0.80225	0.8729