Fig 1.
Proposed methodology.
Fig 2.
Flowchart of bagging DT.
Fig 3.
Flowchart of bagging SVM.
Fig 4.
Flowchart of bagging RF.
Fig 5.
Flowchart of bagging LR.
Fig 6.
Flowchart of bagging NB.
Fig 7.
Flowchart of bagging k-NN.
Table 1.
Dataset variables described (in Raw Form).
Fig 8.
Correlation of dataset variables.
[0pc][-1pc]Figure 8, 10, 11, 12,15, 18 & 21 – The quality of the image is poor and pixelated. Hence please supply a corrected version with an unpixelated typeface.
Fig 9.
P- P-value for different features.
Fig 10.
Distribution of HCV presence in dataset before data refinement (Oversampling and Undersampling).
Fig 11.
Distribution of HCV presence in dataset after undersampling.
Fig 12.
Distribution of HCV presence in dataset after oversampling.
Fig 13.
Flowchart of oversampling process.
Fig 14.
Flowchart of undersampling process.
Table 2.
Comparison of bagging ML methods for oversampled dataset.
Fig 15.
Comparison of evaluation metrics for bagging-ML methods (For Oversampled Data).
Fig 16.
Confusion matrices for bagging ML methods (For Oversampled Dataset).
Fig 17.
AUC for bagging ML methods (For Oversampled Dataset).
Table 3.
Comparison of bagging ML methods for undersampled dataset.
Fig 18.
Comparison of evaluation metrics for bagging-ML methods (For Undersampled Data).
Fig 19.
Confusion matrices for bagging ML methods (For Undersampled Dataset).
Fig 20.
AUC for bagging ML methods (For Undersampled Dataset).
Fig 21.
Comparison of accuracy for bagging-ML methods used in this study.
Table 4.
Highest accuracy of ML methods compared to previous works.