Table 1.
AUC | F1-Score | Precision | Recall | Accuracy | Specificity | Cohen’s Kappa | MCC | |
---|---|---|---|---|---|---|---|---|
DL | 0.987 | 0.737 | 0.875 | 0.636 | 0.958 | 0.991 | 0.715 | 0.725 |
ADA | 0.972 | 0.700 | 0.778 | 0.636 | 0.950 | 0.982 | 0.673 | 0.677 |
BNB | 0.963 | 0.816 | 0.741 | 0.909 | 0.962 | 0.968 | 0.796 | 0.801 |
KNN | 0.938 | 0.744 | 0.762 | 0.727 | 0.954 | 0.977 | 0.719 | 0.719 |
LREG | 0.990 | 0.837 | 0.857 | 0.818 | 0.971 | 0.986 | 0.821 | 0.821 |
RF | 0.977 | 0.792 | 0.677 | 0.955 | 0.954 | 0.954 | 0.767 | 0.782 |
SVC | 0.990 | 0.809 | 0.760 | 0.864 | 0.962 | 0.972 | 0.788 | 0.790 |
XGB | 0.980 | 0.791 | 0.810 | 0.773 | 0.962 | 0.982 | 0.770 | 0.770 |
AUC | F1-Score | Precision | Recall | Accuracy | Specificity | Cohen’s Kappa | MCC | |
DL | 0.966 | 0.769 | 0.750 | 0.789 | 0.925 | 0.950 | 0.724 | 0.725 |
ADA | 0.958 | 0.746 | 0.862 | 0.658 | 0.929 | 0.980 | 0.706 | 0.714 |
BNB | 0.949 | 0.750 | 0.714 | 0.789 | 0.916 | 0.940 | 0.700 | 0.701 |
KNN | 0.954 | 0.762 | 0.696 | 0.842 | 0.916 | 0.930 | 0.712 | 0.716 |
LREG | 0.963 | 0.775 | 0.738 | 0.816 | 0.925 | 0.945 | 0.730 | 0.731 |
RF | 0.972 | 0.821 | 0.800 | 0.842 | 0.941 | 0.960 | 0.786 | 0.786 |
SVC | 0.969 | 0.829 | 0.773 | 0.895 | 0.941 | 0.950 | 0.794 | 0.797 |
XGB | 0.952 | 0.722 | 0.765 | 0.684 | 0.916 | 0.960 | 0.673 | 0.675 |
AUC | F1-Score | Precision | Recall | Accuracy | Specificity | Cohen’s Kappa | MCC | |
DL | 0.920 | 0.735 | 0.782 | 0.694 | 0.870 | 0.932 | 0.650 | 0.652 |
ADA | 0.868 | 0.615 | 0.762 | 0.516 | 0.833 | 0.944 | 0.513 | 0.529 |
BNB | 0.901 | 0.699 | 0.705 | 0.694 | 0.845 | 0.898 | 0.595 | 0.595 |
KNN | 0.918 | 0.774 | 0.707 | 0.855 | 0.870 | 0.876 | 0.684 | 0.690 |
LREG | 0.919 | 0.729 | 0.768 | 0.694 | 0.866 | 0.927 | 0.640 | 0.642 |
RF | 0.928 | 0.762 | 0.750 | 0.774 | 0.874 | 0.910 | 0.677 | 0.677 |
SVC | 0.925 | 0.758 | 0.758 | 0.758 | 0.874 | 0.915 | 0.673 | 0.673 |
XGB | 0.920 | 0.740 | 0.723 | 0.758 | 0.862 | 0.898 | 0.646 | 0.647 |