## 3 Analysis and Results of CGAN-IFCM
### Performance Evaluation of CGAN-IFCM
#### 3.1.2 Multi-Class Detection Model using CGAN-IFCM  
For the construction of the multi-class detection model, the problem is extended to determining the exact type of voice disorders from among four possibilities: healthy, hyperkinetic dysphonia, hypokinetic dysphonia, and reflux laryngitis. First, the SVD database is considered. Table 4 presents the TNR and TPR of selected scenarios with \(N_{c}=[3,10]\), \(\omega_{n},\omega_{0}=[2,2.4]\), and a step size of 1 and 0.1, respectively. In addition to the first two points stated in Table 2, there are three other observations. 1. The performance of CGAN-IFCM is better when \(N_{c}=[6,8]\) compared to \(N_{c}=[3,5]\) and \(N_{c}=[9,10]\). It achieves lower performance when \(N_{c}<6\) and \(N_{c}>8\) as either an insufficient number of clusters or redundant clusters are considered. 2. The detection model is dominated by the class of healthy candidates because the number of samples of healthy candidates is far greater than the number of samples of candidates with hyperkinetic dysphonia, hypokinetic dysphonia, or reflux laryngitis after the adoption of CGAN. In Table 4, there difference between TNR and TPR (where TNR is higher than TPR) is further increased (on average, by 4.56% versus 3.94%, as shown in Table 3) between TNR and TPR, where TPR is higher than TNR. 3. The overall accuracy of the multi-class detection model is lower than that of the binary-class detection model. Basically, the multi-class detection model is a more complicated problem compared to the binary classifier. Further, the issue of an imbalanced dataset is especially significant given the small sample size of patients with hypokinetic dysphonia (2% of the samples of healthy candidates). \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Control Weighting Factors**} & \multicolumn{8}{c}{**TNR (\%)/TPR (\%) with Varying \(N_{c}\)**} \\ \hline \(\omega_{n}\) & \(\omega_{0}\) & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 \\ \hline \multirow{8}{*}{2.2} & 2 & 88.2/91.4 & 91.1/93.8 & 89.9/92.5 & 86.7/91.6 & 84.0/87.4 & 82.1/86.1 & 77.6/80.3 & 72.6/75.6 \\ \cline{2-10}  & 2.1 & 87.2/90.6 & 90.2/93.1 & 89.3/92.1 & 86.9/90.3 & 83.0/86.6 & 81.7/84.0 & 75.1/79.2 & 71.5/74.2 \\ \cline{2-10}  & 2.2 & 86.1/89.4 & 89.2/92.0 & 88.1/91.5 & 85.6/89.4 & 82.4/85.3 & 81.4/83.7 & 74.5/78.6 & 69.3/73.3 \\ \cline{2-10}  & 2.3 & 85.2/88.1 & 88.2/91.3 & 85.7/88.7 & 84.9/88.1 & 81.8/84.7 & 80.1/83.2 & 73.6/76.3 & 68.9/72.1 \\ \cline{2-10}  & 2.4 & 83.1/86.7 & 86.4/90.6 & 83.8/87.9 & 82.8/87.2 & 80.6/83.5 & 78.7/81.9 & 72.3/75.6 & 68.6/71.8 \\ \hline \multirow{8}{*}{2.3} & 2 & 89.2/92.3 & 91.5/94.3 & 90.6/93.4 & 87.8/92.0 & 85.3/88.6 & 82.5/86.8 & 78.0/81.6 & 72.9/76.3 \\ \cline{2-10}  & 2.1 & 87.8/90.9 & 88.6/93.6 & 88.5/92.5 & 87.4/90.5 & 83.6/87.7 & 81.9/84.6 & 75.5/79.8 & 72.1/74.7 \\ \cline{2-10}  & 2.2 & 86.6/90.1 & 88.4/91.5 & 88.2/90.3 & 86.5/89.8 & 83.4/86.3 & 81.6/82.5 & 74.9/79.3 & 70.2/72.5 \\ \cline{2-10}  & 2.3 & 85.5/88.5 & 88.4/91.1 & 86.3/89.4 & 85.7/88.4 & 82.5/85.8 & 80.2/83.1 & 74.2/76.8 & 69.6/72.0 \\ \cline{2-10}  & 2.4 & 84.1/87.3 & 87.1/90.9 & 85.5/88.8 & 84.5/87.8 & 81.1/84.3 & 79.8/80.5 & 73.2/76.2 & 69.1/71.2 \\ \hline \multirow{8}{*}{2.4} & 2 & 87.1/90.4 & 88.5/92.3 & 86.7/89.9 & 85.2/88.0 & 83.8/86.6 & 81.8/85.3 & 76.5/79.8 & 72.5/75.8 \\ \cline{2-10}  & 2.1 & 85.8/88.5 & 87.4/90.7 & 86.2/89.3 & 84.6/87.1 & 83.0/85.9 & 79.7/83.8 & 75.3/78.9 & 71.7/74.9 \\ \cline{1-1} \cline{2-10}  & 2.2 & 85.0/87.5 & 86.5/89.2 & 85.3/88.5 & 84.1/87.0 & 82.2/85.3 & 78.5/82.9 & 74.3/77.0 & 70.4/73.6 \\ \cline{1-1} \cline{2-10}  & 2.3 & 84.2/87.2 & 85.5/88.6 & 84.7/87.5 & 83.6/86.6 & 81.7/84.3 & 79.8/80.3 & 73.7/76.3 & 69.7/72.4 \\ \cline{1-1} \cline{2-10}  & 2.4 & 83.4/86.5 & 84.7/87.3 & 83.7/86.6 & 82.7/85.5 & 82.0/83.7 & 76.3/79.3 & 72.4/75.6 & 68.5/70.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._Next, the multi-class detection model is implemented using the VOICED database.