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. 2021 Feb 18;25(5):1347–1357. doi: 10.1109/JBHI.2021.3060035

TABLE I. The Results for Random Forest, Gradient Boosting, MLP and Our IE-Net in Terms of Accuracy, Recall, Precision and AUC (%).

4]*Methods Evaluation Metrics (mean Inline graphic std)
ACC Recall Precision AUC
GradientBoosting 85.40Inline graphic3.08 79.72Inline graphic5.54 89.31Inline graphic4.06 85.22Inline graphic3.36
Random Forest 84.76Inline graphic3.65 80.62Inline graphic6.77 85.21Inline graphic6.07 84.42Inline graphic4.19
MLP 82.06Inline graphic2.99 74.98Inline graphic5.29 88.03Inline graphic5.25 82.19Inline graphic3.55
IE-Net (zeros) 64.41Inline graphic9.74 92.35Inline graphic2.36 83.05Inline graphic11.65 71.70Inline graphic6.60
IE-Net 94.80Inline graphic1.98 92.79Inline graphic3.07 92.97Inline graphic3.06 94.93Inline graphic2.00