Table 1.
Literature review summary.
Fig 1.
Framework of the methodology.
Fig 2.
The method of stratified K-fold cross-validation and machine learning steps.
Fig 3.
The steps in Stage 2.
Fig 4.
Percentage of accuracy scores for 10000 synthetic data generation.
Table 2.
Data sets information.
Table 3.
Hyperparameters for all models for three datasets.
Table 4.
Performance measures for ML models of original dataset.
Fig 5.
Comparison of Model Performance Metrics of original data.
Fig 6.
AUC ROC curves of all models for original dataset.
Table 5.
Performance measures for ML models of synthesized dataset by Gaussian Copula model.
Fig 7.
Comparison of Model Performance Metrics of GC data.
Fig 8.
AUC ROC curves of all models for Gaussian Copula model synthesized dataset.
Table 6.
Performance measures for ML models of synthesized dataset by TVAE.
Fig 9.
Comparison of Model Performance Metrics of TVAE dataset.
Fig 10.
AUC ROC curves of all models for TVAE dataset.
Table 7.
Best models of Tables 2–4.
Table 8.
Comparison of accuracies with other authors.