Table 2.
Feature Selection | Classifier | Accuracy | Sensitivity | Specificity | Precision | F-Score |
---|---|---|---|---|---|---|
SVM-RFE | LR | 0.7948 | 0.7818 | 0.7532 | 0.7676 | 0.7746 |
RF | 0.7890 | 0.7989 | 0.7984 | 0.8115 | 0.8052 | |
KNN | 0.7342 | 0.6958 | 0.7381 | 0.7961 | 0.7426 | |
MLP | 0.8070 | 0.7936 | 0.7791 | 0.8016 | 0.7976 | |
NN | 0.8197 | 0.8274 | 0.8203 | 0.8387 | 0.8330 | |
XGBoost | 0.8098 | 0.8108 | 0.8310 | 0.8533 | 0.8315 | |
RFFS | LR | 0.7804 | 0.7371 | 0.7422 | 0.8024 | 0.7684 |
RF | 0.8264 | 0.7699 | 0.7338 | 0.8236 | 0.7958 | |
KNN | 0.8048 | 0.7128 | 0.7661 | 0.7753 | 0.7427 | |
MLP | 0.7994 | 0.7808 | 0.7396 | 0.8115 | 0.7959 | |
NN | 0.8507 | 0.8871 | 0.8902 | 0.8522 | 0.8693 | |
XGBoost | 0.8311 | 0.8782 | 0.7984 | 0.8626 | 0.8703 | |
HFS | LR | 0.7834 | 0.7989 | 0.7813 | 0.7959 | 0.7974 |
RF | 0.8362 | 0.7805 | 0.8496 | 0.8115 | 0.7957 | |
KNN | 0.8032 | 0.8018 | 0.7123 | 0.7872 | 0.7944 | |
MLP | 0.8421 | 0.8305 | 0.7513 | 0.8257 | 0.8281 | |
NN | 0.8758 | 0.8518 | 0.8158 | 0.8691 | 0.8604 | |
XGBoost | 0.8812 | 0.8677 | 0.8126 | 0.8737 | 0.8707 |
SVM-RFE: support vector machine recursive feature elimination; RFFS: random forest feature selection; HFS: hybrid feature selection; LR: logistic regression; KNN: k-nearest neighbors; NN: neural network; RF: random forest; MLP: multilayer perceptron; XGBoost: extreme gradient boosting. Highest scores are marked in bold.