Research Article
An Ensemble Feature Selection Approach-Based Machine Learning Classifiers for Prediction of COVID-19 Disease
Table 2
Performance of classifiers before feature selection.
| Datasets | Classifiers | Accuracy | Precision | Recall | -score | AUC |
| Israeli COVID-19 dataset | Decision tree | 0.83 | 0.95 | 0.23 | 0.37 | 0.78 | Naïve Bayes | 0.83 | 0.95 | 0.22 | 0.36 | 0.71 | KNN | 0.83 | 0.95 | 0.22 | 0.36 | 0.74 | MLP | 0.83 | 0.95 | 0.23 | 0.37 | 0.78 | SVM | 0.83 | 0.95 | 0.23 | 0.37 | 0.78 |
| Symptoms and COVID-19 presence dataset | Decision tree | 0.99 | 0.99 | 0.99 | 0.99 | 1.0 | Naïve Bayes | 0.78 | 1.0 | 0.73 | 0.84 | 0.99 | KNN | 0.99 | 1.0 | 0.99 | 0.99 | 1.0 | MLP | 0.98 | 0.98 | 0.99 | 0.99 | 1.0 | SVM | 0.99 | 0.99 | 0.99 | 0.99 | 0.99 |
|
|