Methods | Â | Accuracy | Precision | Recall | F1 | AUC |
---|---|---|---|---|---|---|
Random Forest | Â Training | 0.851 | 1.000 | 0.238 | 0.384 | 0.619 |
 Test | 0.808 | 0.909 | 0.068 | 0.127 | 0.533 | |
Logistic Regression | Â Training | 0.825 | 0.629 | 0.256 | 0.364 | 0.610 |
 Test | 0.808 | 0.567 | 0.260 | 0.357 | 0.605 | |
Lasso Regression | Â Training | 0.825 | 0.762 | 0.148 | 0.248 | 0.568 |
 Test | 0.813 | 0.710 | 0.151 | 0.249 | 0.567 | |
Radial SVM [40] | Â Training | 0.515 | 0.970 | 0.491 | 0.652 | 0.701 |
 Test | 0.337 | 0.896 | 0.204 | 0.333 | 0.586 | |
 Val | 0.806 | 0.849 | 0.920 | 0.883 | 0.642 | |
Gradient boosting [40] | Â Training | 0.851 | 0.934 | 0.899 | 0.916 | 0.690 |
 test | 0.718 | 0.822 | 0.816 | 0.819 | 0.574 | |
 Val | 0.828 | 0.885 | 0.905 | 0.895 | 0.682 | |
Bayes [40] | Â Training | 0.567 | 0.965 | 0.553 | 0.703 | 0.649 |
 Test | 0.465 | 0.861 | 0.405 | 0.551 | 0.562 | |
 Val | 0.828 | 0.891 | 0.895 | 0.893 | 0.713 | |
Linear regression [40] | Â Training | 0.801 | 0.943 | 0.835 | 0.886 | 0.599 |
 Test | 0.679 | 0.828 | 0.763 | 0.794 | 0.541 | |
 Val | 0.788 | 0.885 | 0.842 | 0.863 | 0.689 | |
Linear SVM [40] | Â Training | 0.337 | 0.896 | 0.205 | 0.333 | 0.586 |
 Test | 0.467 | 0.861 | 0.407 | 0.553 | 0.586 | |
 Val | 0.818 | 0.873 | 0.906 | 0.889 | 0.676 | |
Sofa Score [13] DCQMFF (proposed) | Â All data | 0.752 | 0.371 | 0.327 | 0.348 | 0.807 |
 Training | 0.822 | 0.822 | 0.821 | 0.822 | 0.896 | |
 Test | 0.821 | 0.812 | 0.812 | 0.812 | 0.885 | |
 Val | 0.775 | 0.764 | 0.754 | 0.759 | 0.849 | |
CNN (Proposed) | Â Training | 0.928 | 0.924 | 0.856 | 0.888 | 0.953 |
 Test | 0.924 | 0.887 | 0.845 | 0.865 | 0.947 | |
 Val | 0.834 | 0.825 | 0.818 | 0.821 | 0.909 |