# Tabular Data Generation to Improve Classification of Liver Disease Diagnosis
## 7 Experiments and Evaluation
### Experimental Results
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Research** & **Title** & **Method and Results** \\ \hline
[19] & \
\begin{tabular}{c} Accurate liver disease \\ prediction system using \\ convolutional neural network \\ \end{tabular} &
\begin{tabular}{c} MCNN-LDPS: 90.75\% \\ M.L.P.N.N.: 86.70\% \\ \end{tabular} \\ \hline
[20] & \
\begin{tabular}{c} Statistical Analysis and \\ Identification of Important \\ Factors \\ of Liver Disease using \\ Machine Learning and Deep \\ Learning Architecture. \\ \end{tabular} &
\begin{tabular}{c} ANN 76.07\%, DTRE 76.07\%, \\ R.Forest 74.36\%, SVM 74.35\%, \\ MLP 74.36\%, GNB 74.50\%, \\ KNN 78.63\%, \\ Logistic Regression 73.50\% \\ \end{tabular} \\ \hline
[21] & \
\begin{tabular}{c} Prediction of Liver Malady \\ Using Advanced \\ Classification Algorithms \\ \end{tabular} &
\begin{tabular}{c} ANN 94.09\% \\ SVM 78.09\% \\ \end{tabular} \\ \hline
[22] & \
\begin{tabular}{c} Prediction of Liver Disease \\ using Rprop, S.A.G. and CNN \\ \end{tabular} &
\begin{tabular}{c} Rprop: 69.41\% \\ S.A.G.: 68.2\% \\ CNN: 96.07\% \\ \end{tabular} \\ \hline
[23] & \
\begin{tabular}{c} Software-based prediction of \\ liver disease with feature \\ selection and classification \\ techniques \\ \end{tabular} &
\begin{tabular}{c} LR, SMO, RF, NB, J48, IBk. \\ The best result is L.R.: 77.4\% \\ \end{tabular} \\ \hline
[40] & \
\begin{tabular}{c} Supervised Machine Learning \\ Models for Liver Disease Risk \\ Prediction \\ \end{tabular} &
\begin{tabular}{c} F-measure of 80.1\%, a precision of \\ 80.4\%, and an A.U.C. equal to 88.4\% \\ after SMOTE with 10-fold \\ cross-validation. \\ \end{tabular} \\ \hline
[41] & \
\begin{tabular}{c} A Hybrid Machine Learning \\ algorithm for Heart and Liver \\ Disease Prediction Using \\ Modified Particle Swarm \\ Optimization with Support \\ Vector Machine \\ \end{tabular} &
\begin{tabular}{c} F-measure of 80.1\%, a precision of \\ 80.4\%, and an A.U.C. equal to 88.4\% \\ after SMOTE with 10-fold \\ cross-validation. \\ \end{tabular} \\ \hline
[42] & \
\begin{tabular}{c} Statistical Machine Learning \\ Approaches to Liver Disease \\ Prediction \\ \end{tabular} &
\begin{tabular}{c} The RF: 98.14\% accuecuecurey. \\ \end{tabular} \\ \hline
[43] & \begin{tabular}{c} Prediction of fatty liver \\ disease using machine \\ learning algorithms \\ \end{tabular} & \begin{tabular}{c} The accuracy of R.F., NB, ANN, and \\ LR 87.48, 82.65, 81.85, and 76.96\%. \\ \end{tabular} \\ \hline Proposed approach & \
\begin{tabular}{c} Tabular Data Generation to \\ Improve Classification of \\ Liver Disease Diagnosis \\ \end{tabular} &
\begin{tabular}{c} ANN: 0.932 with SMOTE \\ SVM: 0.9328 with SMOTE \\ LR: 0.9872 with SMOTE \\ DT: 0.9788 with SMOTE \\ K-NN: 0.996 with SMOTE \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison with other research work. Table 5 shows that all cases achieved around 0.75% of accuracy are observed, and for all Class 2 cases, including NO-AUG, DD-AUG, and TD-AUG we note the decrease in the accuracy of the samples. As regards F-measurement, results are 0.84. The output ranged in precision from 0.71306 to 0.71786 for the TD-AUG sets. Given these data, we can conclude that this classification is an inaccurate method for identifying liver sickness in individuals who are otherwise healthy. This is because it fails to account for individual variations in liver function. On the other hand, in comparison to GAN, SMOTE demonstrates better stability in terms of improvement in accuracy. Despite this, we found that employing L.R. with SMOTE performs much better than GAN, with an average accuracy of 98% across all cases. In Table 6, all cases that achieved around 0.71% of accuracy are observed, and for all Class 2 cases NO-AUG, DD-AUG, and TD-AUG, we note the increase in the accuracy of the samples. With regard to F-measurement, the results are 0.80. The output ranged in precision from 0.75004 to 0.75471 for the TD-AUG sets. Although GAN improves accuracy, SMOTE shows a loss in overall accuracy.