## 4 Performance Evaluation and Comparison
### Comparison between Proposed and Related Work  
Based on our finding, the multinomial classification of appendicitis, acute appendicitis, duodenitis, and pancreatitis is the first of its kind. Previous works [10; 11] have considered the problem as binary classification. To compare the performance between the proposed and related work [10; 11], it is analyzed in two directions. (i) Table 4 gives the raw comparison between the works. (ii) Table 5 gives the matched comparison between the works. The forms of the datasets, application, and cross-validation in [10; 11] will be changed into those in this paper. Thus, every work considers 5600 samples for multinomial classification and evaluates using 10-fold cross validation. From the raw comparison, it can be seen that the performance, \(S_{e}\), \(S_{p}\), and Acc of classification between [10] and [11] are similar for binary classification between healthy and appendicitis candidates, and between healthy and duodenitis candidates. For that between healthy and acute appendicitis, and between healthy and pancreatitis, the improvements are 8% and 9%, respectively. If the proposed work is taken into account, it outperforms [10] in the classification of all inflammations. By averaging the Acc in [11], it is approximately equal to the proposed work. Therefore, it can be interpreted that multinomial classification can be achieved without deteriorating the performance in inflammations classification. A matched comparison environment is setup to compare the performance between algorithms in organ inflammations classification. Repeated simulation is carried out for [10; 11; 14; 32] while using the identical datasets and 10-fold cross validation. It is concluded that the proposed MOGA-SVM improves the Acc from 6.9% to 13.4%. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Kernel**} & \multicolumn{3}{c}{**Performance**} \\ \cline{2-4}  & **Scenario (i)** & **Scenario (ii)** & **Scenario (iii)** \\  & **(\(S_{e}S_{p}\),Acc)\%** & **(\(S_{e}S_{p}\),Acc)\%** & **(\(S_{e}S_{p}\),Acc)\%** \\ \hline \(k_{1}\)(\(x_{i}\),\(x_{j}\)) & (57.6, 58.2, 57.9) & (57.7, 57.1, 57.4) & (58.8, 60.4, 59 \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Work** & **Method** & **Feature Extraction** & **Dataset (Samples)** &
\begin{tabular}{c} **Cross** \\ **Validation** \\ \end{tabular} & **Class Labels** & \(\mathbf{S_{c}}\) (\%) & \(\mathbf{S_{p}}\) (\%) & **Acc (\%)** \\ \hline \multirow{8}{*}{[10]} & Binary Classification & & & Class 0: healthy: & & 81.8 & 93.3 & 91.2 \\  & using modified & Mean and standard & Healthy (100), & & & Class 1: appendicitis & 76.5 & 82.4 & 80.8 \\  & auto-greressive model & deviation of & & & & Class 1: acute appendicitis & & \\  & and linear kernel & prediction error & & & & & \\  & SVM & & & & & & \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\  & & peak systolic velocity; & & & & & \\  & reverse velocity; peak & Healthy (100), & & & Class 0: healthy: & & \\  & using RBF SVM & & & & & & \\  & duration of systolic; & & & & & \\  & and duration of & & & & & \\  & & diastole velocity; and & & & & & \\  & & & & & & \\  & & and duration of & & & & & \\  & & diastole & & & & \\ \hline \multirow{8}{*}{Our work} & Multinomial & Cross-correlation and & Healthy (1800), & & & Class 0: health: & \\  & Classification using & convolution & Acute Appendicitis & & Class 1: appendicitis & & \\  & customized kernel & coefficients & & & & \\  & & coefficients & & & & \\  & & & & & & \\  & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Raw comparison between proposed and related work [10, 11].