Evaluation metrics | Missing proportions | Machine learning methods | Traditional methods | ||||||
---|---|---|---|---|---|---|---|---|---|
LR | RF | NN | SVM | EL | Mode | KNN | MICE | ||
Sensitivity | 0.05 | 0.877 | 0.866 | 0.866 | 0.866 | 0.889 | 0.851 | 0.866 | 0.869 |
 | 0.10 | 0.858 | 0.874 | 0.874 | 0.874 | 0.881 | 0.862 | 0.874 | 0.868 |
 | 0.15 | 0.889 | 0.877 | 0.904 | 0.874 | 0.897 | 0.858 | 0.866 | 0.870 |
 | 0.20 | 0.885 | 0.874 | 0.877 | 0.866 | 0.889 | 0.843 | 0.866 | 0.877 |
 | 0.30 | 0.866 | 0.897 | 0.920 | 0.881 | 0.923 | 0.739 | 0.866 | 0.876 |
 | 0.50 | 0.862 | 0.943 | 0.973 | 0.900 | 0.969 | 0.693 | 0.739 | 0.789 |
 | Average | 0.873 | 0.889 | 0.902 | 0.877 | 0.908 | 0.808 | 0.846 | 0.858 |
AUC | 0.05 | 0.913 | 0.912 | 0.913 | 0.912 | 0.913 | 0.901 | 0.912 | 0.911 |
 | 0.10 | 0.909 | 0.914 | 0.915 | 0.914 | 0.911 | 0.904 | 0.915 | 0.912 |
 | 0.15 | 0.919 | 0.917 | 0.924 | 0.916 | 0.919 | 0.893 | 0.911 | 0.913 |
 | 0.20 | 0.913 | 0.918 | 0.916 | 0.914 | 0.916 | 0.891 | 0.915 | 0.912 |
 | 0.30 | 0.902 | 0.921 | 0.933 | 0.921 | 0.934 | 0.860 | 0.910 | 0.907 |
 | 0.50 | 0.887 | 0.952 | 0.947 | 0.942 | 0.950 | 0.855 | 0.860 | 0.875 |
 | Average | 0.907 | 0.922 | 0.925 | 0.920 | 0.924 | 0.884 | 0.904 | 0.905 |
Kappa | 0.05 | 0.562 | 0.555 | 0.555 | 0.555 | 0.569 | 0.519 | 0.555 | 0.555 |
 | 0.10 | 0.547 | 0.557 | 0.557 | 0.557 | 0.561 | 0.526 | 0.557 | 0.554 |
 | 0.15 | 0.565 | 0.558 | 0.574 | 0.555 | 0.591 | 0.507 | 0.551 | 0.561 |
 | 0.20 | 0.568 | 0.561 | 0.563 | 0.556 | 0.592 | 0.506 | 0.557 | 0.564 |
 | 0.30 | 0.547 | 0.566 | 0.579 | 0.556 | 0.619 | 0.491 | 0.547 | 0.569 |
 | 0.50 | 0.507 | 0.627 | 0.630 | 0.622 | 0.645 | 0.556 | 0.491 | 0.514 |
 | Average | 0.549 | 0.571 | 0.576 | 0.567 | 0.596 | 0.518 | 0.543 | 0.553 |