Table 6 Performance of data mining models using 5-fold
From: Presenting a prediction model for HELLP syndrome through data mining
Classifier | Accuracy | Precision | Sensitivity | Specificity | F1-score | AUC |
|---|---|---|---|---|---|---|
DL | 0.989 | 0.979 | 1 | 0.978 | 0.989 | 1 |
MLP | 0.974 | 0.956 | 0.997 | 0.952 | 0.975 | 1 |
DT | 0.722 | 0.647 | 0.978 | 0.465 | 0.778 | 0.723 |
SVM | 0.906 | 0.886 | 0.932 | 0.881 | 0.908 | 0.971 |
KNN | 0.969 | 0.947 | 0.994 | 0.944 | 0.970 | 0.981 |
RF | 0.961 | 0.945 | 0.978 | 0.944 | 0.961 | 0.991 |
Adaboost | 0.986 | 0.981 | 0.992 | 0.981 | 0.986 | 1 |
xgboost | 0.964 | 0.988 | 0.964 | 0.964 | 0.976 | 0.991 |
LR | 0.951 | 0.963 | 0.969 | 0.903 | 0.966 | 0.983 |