Evaluation metrics | Missing proportions | Machine learning methods | Traditional methods | ||||||
---|---|---|---|---|---|---|---|---|---|
LR | RF | NN | SVM | EL | Mode | KNN | MICE | ||
sensitivity | 0.05 | 0.870 | 0.874 | 0.874 | 0.870 | 0.874 | 0.866 | 0.870 | 0.871 |
 | 0.10 | 0.877 | 0.877 | 0.877 | 0.877 | 0.877 | 0.866 | 0.877 | 0.873 |
 | 0.15 | 0.885 | 0.881 | 0.889 | 0.881 | 0.889 | 0.866 | 0.877 | 0.882 |
 | 0.20 | 0.874 | 0.874 | 0.874 | 0.874 | 0.877 | 0.870 | 0.870 | 0.875 |
 | 0.30 | 0.897 | 0.885 | 0.877 | 0.874 | 0.900 | 0.805 | 0.870 | 0.878 |
 | 0.50 | 0.885 | 0.866 | 0.866 | 0.866 | 0.893 | 0.766 | 0.866 | 0.852 |
 | Average | 0.881 | 0.876 | 0.876 | 0.874 | 0.885 | 0.840 | 0.872 | 0.872 |
AUC | 0.05 | 0.912 | 0.914 | 0.914 | 0.912 | 0.915 | 0.911 | 0.912 | 0.913 |
 | 0.10 | 0.917 | 0.916 | 0.916 | 0.916 | 0.917 | 0.917 | 0.917 | 0.914 |
 | 0.15 | 0.910 | 0.916 | 0.917 | 0.915 | 0.917 | 0.915 | 0.914 | 0.915 |
 | 0.20 | 0.912 | 0.914 | 0.914 | 0.914 | 0.916 | 0.909 | 0.911 | 0.913 |
 | 0.30 | 0.921 | 0.919 | 0.913 | 0.913 | 0.922 | 0.911 | 0.908 | 0.914 |
 | 0.50 | 0.924 | 0.914 | 0.913 | 0.915 | 0.925 | 0.912 | 0.916 | 0.909 |
 | Average | 0.916 | 0.916 | 0.915 | 0.914 | 0.919 | 0.913 | 0.913 | 0.913 |
Kappa | 0.05 | 0.556 | 0.558 | 0.558 | 0.556 | 0.558 | 0.564 | 0.556 | 0.556 |
 | 0.10 | 0.564 | 0.564 | 0.564 | 0.564 | 0.564 | 0.580 | 0.566 | 0.562 |
 | 0.15 | 0.560 | 0.557 | 0.562 | 0.557 | 0.562 | 0.580 | 0.564 | 0.557 |
 | 0.20 | 0.554 | 0.554 | 0.555 | 0.554 | 0.556 | 0.539 | 0.552 | 0.548 |
 | 0.30 | 0.572 | 0.565 | 0.571 | 0.559 | 0.574 | 0.629 | 0.564 | 0.560 |
 | 0.50 | 0.560 | 0.548 | 0.549 | 0.548 | 0.564 | 0.632 | 0.552 | 0.540 |
 | Average | 0.561 | 0.558 | 0.560 | 0.556 | 0.563 | 0.587 | 0.559 | 0.554 |