# Tabular Data Generation to Improve Classification of Liver Disease Diagnosis
## 7 Experiments and Evaluation
### Experimental Results
The output ranged in precision from 0.73105 to 0.76553 for the TD-AUG sets. Based on these findings, we may deduce that this classification does not provide satisfactory outcomes and is hence unsuitable for use by healthy individuals in the diagnosis of liver disease in patients with varying degrees of illness. SMOTE, on the other hand, is superior to additional data augmentation in terms of its ability to enhance accuracy. Additionally, improvements may be noticed in terms of \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{3}{*}{**ANN**} & \multirow{3}{*}{**Case**} & \multicolumn{4}{c}{**GAN**} & \multicolumn{4}{c}{**SMOTE**} \\ \cline{3-10}  & & **Accuracy** & **Recall** & **Precision** & **F-Measure** & **Accuracy** & **Recall** & **Precision** & **F-Measure** \\ \cline{2-10}  & NO-AUG & 0.6964 & 0.85817 & 0.75158 & 0.80135 & 0.5588 & 0.559 & 0.559 & 0.549 \\ \cline{2-10}  & DD-AUG & 0.7024 & 0.8754 & 0.75471 & 0.80002 & 0.5473 & 0.547 & 0.546 & 0.546 \\ \cline{2-10}  & TD-AUG & 0.7094 & 0.8813 & 0.75004 & 0.80081 & 0.5035 & 0.504 & 0.527 & 0.499 \\ \cline{2-10}  & AVG & 0.7059 & 0.8784 & 0.75238 & 0.80042 & 0.5254 & 0.526 & 0.537 & 0.523 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Evaluation of ANN Classifier for NO-AUG, DD-AUG and TD-AUG. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{3}{*}{**K.N.N.**} & \multirow{3}{*}{**Case**} & \multicolumn{4}{c}{**GAN**} & \multicolumn{4}{c}{**SMOTE**} \\ \cline{3-10}  & & **Accuracy** & **Recall** & **Precision** & **F-Measure** & **Accuracy** & **Recall** & **Precision** & **F-Measure** \\ \cline{2-10}  & NO-AUG & 0.67067 & 0.8101 & 0.74889 & 0.77829 & 0.9907 & 0.991 & 0.991 & 0.991 \\ \cline{2-10}  & DD-AUG & 0.69455 & 0.8145 & 0.75321 & 0.78524 & 0.9951 & 0.995 & 0.995 & 0.995 \\ \cline{2-10}  & TD-AUG & 0.69122 & 0.8784 & 0.77001 & 0.79245 & 0.9968 & 0.997 & 0.997 & 0.997 \\ \cline{2-10}  & AVG & 0.69289 & 0.8465 & 0.76161 & 0.78885 & 0.996 & 0.996 & 0.996 & 0.996 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Evaluation of kNN Classifier for NO-AUG, DD-AUG and TD-AUG. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{3}{*}{**L.R.**} & \multirow{3}{*}{**Case**} & \multicolumn{4}{c}{**GAN**} & \multicolumn{4}{c}{**SMOTE**} \\ \cline{3-10}  & & **Accuracy** & **Recall** & **Precision** & **F-Measure** & **Accuracy** & **Recall** & **Precision** & **F-Measure** \\ \cline{2-10}  & NO-AUG & 0.74889 & 0.9976 & 0.71306 & 0.83166 & 0.9624 & 0.962 & 0.963 & 0.962 \\ \cline{2-10}  & DD-AUG & 0.75451 & 0.9862 & 0.71786 & 0.83517 & 0.9851 & 0.985 & 0.985 & 0.985 \\ \cline{2-10}  & TD-AUG & 0.75007 & 0.9954 & 0.71724 & 0.84006 & 0.9893 & 0.989 & 0.989 & 0.989 \\ \cline{2-10}  & AVG & 0.75229 & 0.9908 & 0.71755 & 0.83762 & 0.9872 & 0.987 & 0.987 & 0.987 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Evaluation of L.R. Classifier for NO-AUG, DD-AUG and TD-AUG.  
accuracy, recall, and F-measure. SMOTE's average accuracy of 97% is a gain in accuracy of 38% when compared to GAN's average accuracy of 59%. In Table 4, all cases that achieved approximately 0.69% of accuracy are observed, ad for all Class 2 cases, such as NO-AUG, DD-AUG, and TD-AUG, we note the decrease in the accuracy of the samples. With regard to F-measurement, the results are 0.79. The output ranged in precision from 0.74889 to 0.77001 for the TD-AUG sets. Based on these data, it appears that the existing classification scheme is inappropriate for discriminating between the various phases of liver disease in otherwise healthy individuals. However, in basic data augmentation scenarios, SMOTE exhibits a greater improvement in outcomes than GAN, with an average accuracy of 99%.