Table 2.
Model performance comparison between related and our work.
| Study | The methods | Accuracy | MCC |
|---|---|---|---|
| Efficient machine-learning model (63) | RF | 0.947 | 0.945 |
| SVM | 0.93 | 0.917 | |
| Lasso-DNN method (69) | SVM | 0.81 | - |
| ANN | 0.9277 | - | |
| Lasso with random forest (70) | RF | 0.9809 | - |
| Repurposed drugs for COVID-19 using AI and ML (71) | RF | 0.82 | - |
| SVM | 0.90 | - | |
| Our proposed work | RF | 0.9931 | 0.9863 |
| TE | 0.9904 | 0.9809 |
The models from the related were listed along with their performance parameters. The accuracy and MCC of the Random Forest and Tree ensemble classifiers were matched with the state-of-the-art methods from the literature.