Research Article
An Ensemble Feature Selection Approach-Based Machine Learning Classifiers for Prediction of COVID-19 Disease
Table 3
Performance of classifiers after feature selection.
| Datasets | Classifiers | Accuracy | Precision | Recall | -score | AUC |
| Israeli COVID-19 dataset | Decision tree | 0.88 | 1.0 | 0.77 | 0.87 | 0.93 | Naïve Bayes | 0.88 | 1.0 | 0.77 | 0.87 | 0.9 | KNN | 0.88 | 1.0 | 0.77 | 0.87 | 0.88 | MLP | 0.88 | 1.0 | 0.77 | 0.87 | 0.93 | SVM | 0.88 | 1.0 | 0.77 | 0.87 | 0.9 |
| Symptoms and COVID-19 presence dataset | Decision tree | 0.97 | 0.97 | 1.0 | 0.98 | 1.0 | Naïve Bayes | 0.78 | 1.0 | 0.72 | 0.84 | 0.99 | KNN | 0.98 | 0.98 | 0.99 | 0.99 | 0.98 | MLP | 0.97 | 0.97 | 1.0 | 0.98 | 1.0 | SVM | 0.97 | 0.97 | 1.0 | 0.98 | 0.99 |
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