# Tabular Data Generation to Improve Classification of Liver Disease Diagnosis
## 7 Experiments and Evaluation
### Experimental Results  
In this work, various measures are applied to evaluate differential evolution's performance and efficiency and to build upon different algorithms for supervised machine learning. This section focuses on how measures in different types and situations are implemented. The results of tests conducted to assess the proposed method's effectiveness are presented, including a study of the impact of different distance methods on classification accuracy. Information about the performance of five different types of classifiers, including (ANN), (SVM), (L.R. ), (D.T. ), and K-nearest neighbor (K-NN) with data augmentation (NO-AUG), double data augmentation (DD-AUG), and triple data augmentation (TD-AUG), is provided in Tables 2-7. All experimental results are based on the average of 10-fold cross-validation. The results of the SVM technique are presented in Table 2, and the experiments conducted with different data set increments showed little difference in results. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**SVM**} & \multirow{2}{*}{**Case**} & \multicolumn{5}{c}{**GAN**} & \multicolumn{5}{c}{**SMOTE**} \\ \cline{3-10}  & & **Accuracy** & **Recall** & **Precision** & **F-measure** & **Accuracy** & **Recall** & **Precision** & **F-Measure** \\ \cline{2-10}  & NO-AUG & 0.70669 & 0.98558 & 0.71304 & 0.82745 & 0.8237 & 0.824 & 0.832 & 0.822 \\ \cline{2-10}  & DD-AUG & 0.71254 & 0.98745 & 0.72004 & 0.83215 & 0.9182 & 0.918 & 0.923 & 0.918 \\ \cline{2-10}  & TD-AUG & 0.71689 & 0.989104 & 0.73041 & 0.83545 & 0.9473 & 0.947 & 0.950 & 0.947 \\ \cline{2-10}  & AVG (AUG) & 0.71472 & 0.988277 & 0.72523 & 0.8338 & 0.9328 & 0.933 & 0.937 & 0.933 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Evaluation of SVM Classifier for NO-AUG, DD-AUG and TD-AUG. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**D.T.**} & \multirow{2}{*}{**Case**} & \multicolumn{5}{c}{**GAN**} & \multicolumn{5}{c}{**SMOTE**} \\ \cline{3-10}  & & **Accuracy** & **Recall** & **Precision** & **F-measure** & **Accuracy** & **Recall** & **Precision** & **F-Measure** \\ \cline{2-10}  & NO-AUG & 0.60163 & 0.71875 & 0.73105 & 0.72485 & 0.9462 & 0.946 & 0.948 & 0.946 \\ \cline{2-10}  & DD-AUG & 0.59455 & 0.74575 & 0.76924 & 0.74674 & 0.9746 & 0.975 & 0.975 & 0.975 \\ \cline{2-10}  & TD-AUG & 0.59278 & 0.7575 & 0.76553 & 0.74934 & 0.9829 & 0.983 & 0.983 & 0.983 \\ \hline \multicolumn{1}{c}{AVG} & 0.59367 & 0.75163 & 0.76739 & 0.74804 & 0.9788 & 0.979 & 0.979 & 0.979 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Evaluation of D.T. Classifier for NO-AUG, DD-AUG and TD-AUG. In Table 2, all cases that achieved approximately 0.71% accuracy are observed for all Class 2 cases, such as NO-AUG, DD-AUG, and TD-AUG. With regard to the F-measurement, the results are 0.83. The output ranged in precision from 0.71304 to 0.73041 for the TD-AUG sets. On the basis of these results, we can conclude that this classification does not have good results that make it an inappropriate way for people who are healthy to diagnose liver disease in terms of the different status of the patient. SMOTE, on the other hand, outperforms GAN in terms of accuracy, with an increase ranging from 12 to 22%. Nonetheless, GAN outperformed SMOTE in terms of precision recall and F-measure. In light of this, GAN outperforms SMOTE in terms of model stability. Notably, the accuracy increase with data augmentation is quite stable. For clarity, accuracy improves somewhat as data creation progresses. Additionally, the average of evaluation metrics was calculated to emphasize the performance of data augmentation techniques. The findings reveal that SMOTE surpasses GAN with an average accuracy of 93% across all data augmentation cases. In Table 3, all cases achieved around 0.60% of accuracy are observed, ad for all Class 2 cases NO-AUG, DD-AUG and TD-AUG we note the decrease in the accuracy of the samples. As regards F-measurement, the results are 0.74.