Table 1.
Summary of related work on COVID-19 identification from blood samples.
| Ref. | Methods | Feature Selection Methods | Metrics (Value) | Data Samples (COVID-19 Samples) |
|---|---|---|---|---|
| [42] | Ensemble learning extra trees, random forest (RF), logistic regression (LR), extreme gradient boosting (ERLX) classifier | Manual | Accuracy: 99.88% AUC: 99.38%, Sensitivity: 98.72% Specificity: 99.99% |
5644 (559) |
| [47] | Categorical gradient boosting (CatBoost), support vector machine (SVM), and LR | Manual | AUC: 89.9–95.8% Specificity: 91.5–98.3% Sensitivity: 55.5–77.8% |
5148 (447) |
| [53] | Ensemble learning with RF, LR, XGBoost, Support Vector Machine (SVM), MLP | Decision Tree Explainer (DTX) | Accuracy (0.88 ± 0.02) |
608 (84) |
| [39] | Artificial Neural Network (ANN) predictive model | Pearson and Kendall correlation coefficient | Area under curve (AUC) values of 0.953 (0.889–0.982). | 151 |
| [35] | ANN, RF, gradient boosting trees, LR and SVM | NA | AUC: 0.85; Sensitivity: 0.68; Specificity: 0.85; Brier Score: 0.16 | 235 (102) |
| [54] | RF classifier | manual | Accuracy: 96.95%, Sensitivity: 95.12%, Specificity: 96.97% |
253 (105) |
| [55] | ANN, Convolutional Neural Network (CNN), Long-Short Term Memory (LSTM), Recurrent Neural Network (RNN), CNN-LSTM, and CNN-RNN | CNN and LSTM | AUC: 0.90, Accuracy: 0.9230, FI-score: 0.93, Precision: 0.9235, Recall: 0.9368 | 600 (80) |
| [56] | SVM, LR, DT, RF and deep neural network (DNN) | Logistic regression (LR) | Accuracy: 91%, Sensitivity: 87%, AUC: 97.1%, Specificity: 95%. |
921 (361) |
| [57] | ANN, CNN, RNN | SMOTE | Accuracy: 94.95%, F1-score: 94.98%, precision: 94.98%, recall: 94.98%, AUC: 100% |
600 (80) |
| [31] | LR | Maximum relevance minimum redundancy (mRMR) algorithm | Sensitivity: 98%, Specificity: 91% |
110 (51) |
| [58] | LR, DT, RF, gradient boosted decision tree | NA | Sensitivity: 75.8%, Specificity: 80.2%, AUC: 85.3% |
3346 (1394) |