## 4 Experimental Results and Discussion
In addition, in each data sample of the men or women, 181 were used for the training dataset and 78 were used for the test dataset. Each set was randomly selected from the subset for a fivefold cross-validation scheme [1; 2; 3; 4]. The whole process was repeated 10 times, and the results were averaged. The results are shown as the means and standard deviations. In Table 5, each column shows the highest values (thick and blue font) obtained within each type of testing parameter, classifier, and sex. Many thick and red values in the rows show the highest results according to various parameters among all women and men. The results of the individual classifiers were also compared by evaluating those of each vowel separately. The best performance results among the vowels are highlighted in thickness in each column and row in Table 5. The accuracy of a model is usually determined after the model parameters are learned and fixed and the learning is not performed. In this paper, the test samples were fed to the model and the number of mistakes that the model made were recorded, after comparison to the actual targets. Then, the percentage of misclassification was calculated. For example, if the number of test samples was 155 and the model classified 130 of those correctly, then the model's accuracy was 83.9%. In this paper, the accuracy will be shown through a confusion matrix. For the FNN classifier in Table 5, a combination of MFCCs and MFCC deltas showed the best performance, 80.13%, for classifying pathological and normal voices in the men's vowel /a/. The average accuracy, 76.92%, was also used with the MFCCs + HOSs parameter in mixed data samples of women and men. In addition, the average performance was 76.28% for the women's data samples regarding the MFCCs + MFCC deltas parameter. The vowel /i/ achieved the greatest accuracy, 80.77%, in the classification between pathological and normal voices using a combination of MFCCs and HOSs for women's data samples. Additionally, in mixed and men data samples, accuracies of 75.64% and 75.00% were obtained from the MFCCs + MFCC deltas + HOSs and MFCCs + HOSs parameters, respectively. In the vowel /u/, the highest accuracy, 80.77%, was achieved by utilizing the LPCCs + HOSs parameter in the case of men's data samples. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Women**} & \multicolumn{2}{c}{**Men**} \\ \hline  & & **Normal** & **Pathological** & **Normal** & **Pathological** \\ \hline \multirow{8}{*}{Normalized skewness} & Mean & 0.28 & 0.06 & 0.13 & \(-\)0.12 \\  & Minimum value & \(-\)0.58 & \(-\)1.15 & \(-\)1.10 & \(-\)1.69 \\  & Maximum value & 1.27 & 1.58 & 1.19 & 1.01 \\  & Percentile 25 & 0.08 & \(-\)0.23 & \(-\)0.07 & \(-\)0.34 \\  & 50 & 0.25 & 0.04 & 0.11 & \(-\)0.10 \\  & 75 & 0.48 & 0.35 & 0.33 & 0.12 \\  & \(p\)-value & 0.000 * & & 0.000 * & \\ \hline \hline \end{tabular} Note: * \(p\) < 0.05.  
\end{table} 
Table 4: _Cont._For the CNN classifiers, utilization of the LPCCs parameter was 82.69%, showing the best performance in classifying pathological and normal voices when using men's /u/ vowel. The vowel /a/ achieved good accuracy, 76.60%, regarding the differentiation between pathological and normal voices, with a combination of MFCCs and HOSs for mixed data samples. Moreover, 76.28% and 75.64% were also obtained from the MFCCs + MFCC deltas + HOSs and MFCCs parameters in both women's and men's data samples, respectively. When using the vowel /i/, similar results, 76.92%, were found for women and men with MFCCs and LPCCs + HOSs, respectively. In mixed data samples of women and men, an average accuracy, 75.00%, was also obtained from the MFCCs + MFCC deltas + HOSs parameters. In the vowel /u/, the highest accuracy, 82.69%, was achieved by the utilization of the LPCCs parameter for data samples from men. In addition, in mixed and women data samples, accuracies of 70.83% and 76.92% were achieved by the LPCCs + HOSs and MFCCs + MFCC deltas + HOSs parameters, respectively.