Table 6 Model performance indicators when applying variable selection with the threshold of 0.05.
AUC ± Standard Deviation | Accuracy | Precision | Recall | |
|---|---|---|---|---|
Stochastic Gradient Descent (SGD) | 0.823 ± 0.040 | 0.699 | 0.746 | 0.710 |
Support Vector Machine (SVM) | 0.792 ± 0.035 | 0.716 | 0.719 | 0.744 |
Decision Tree (DT) | 0.782 ± 0.039 | 0.728 | 0.745 | 0.722 |
Ramdom Forest (RF) | 0.859 ± 0.027 | 0.778 | 0.803 | 0.764 |
Linear Regression (LINEAR), adapted | 0.802 ± 0.019 | 0.721 | 0.726 | 0.742 |
Logistic Regression (LOGIT) | 0.822 ± 0.034 | 0.725 | 0.741 | 0.724 |
Artificial Neural Network (ANN) | 0.935 ± 0.026 | 0.828 | 0.828 | 0.849 |
