## 4 Discussion  
The main purpose of this study was to identify the prognostic factors that influence the effectiveness of speech therapy in individuals with speech disorders. In the comparison of pre- and post-voice-therapy aspects, for females, significant differences were observed in terms of jitter (\(p<0.001\)), shimmer (\(p<0.001\)), and NHR (\(p=0.004\)), whereas, for males, significant differences were observed for jitter (\(p<0.001\)), shimmer (\(p=0.013\)), and MPT (\(p=0.004\)). In terms of the effectiveness of voice therapy before and after treatment, for females, the jitter (\(p<0.001\)), shimmer (\(p<0.001\)), and NHR (\(p=0.018\)) parameters demonstrated positive efficacy. Similarly, for males, the jitter (\(p<0.001\)), shimmer (\(p=0.002\)), and MPT (\(p=0.002\)) parameters showed favorable effectiveness. Based on this experiment, we were able to identify gender-specific efficacy factors for assessing the effectiveness of voice therapy before and after treatment. The results indicated that the jitter, shimmer, and NHR parameters were significant efficacy indicators for both males and females. This suggests the necessity of conducting gender-specific experiments to accurately evaluate the effectiveness of voice therapy. Two binomial logistic regression analyses were conducted to examine the effectiveness of voice therapy in relation to various acoustic and subjective habit-related variables. For females, the binomial logistic regression model consisting of coffee status, jitter after treatment, jitter comparison, and shimmer before treatment (\(p=0.025\), \(p=0.039\), \(p=0.027\), and \(p=0.026\), respectively) effectively explained its efficacy. According to the real values of the model, the regression model was found to be statistically significant (\(p<0.001\)); the Cox and Snell R\({}^{2}\) value was 0.502 and the Nagelkerke R\({}^{2}\) value was 0.682, indicating that this model has an explanatory power of approximately 68% for the dependent variables included in the study. For males, the regression model was found to be statistically significant (\(p=0.005\)), and the model had an explanatory power of approximately 33% (Cox and Snell R\({}^{2}\) value = 0.232, Nagelkerke R\({}^{2}\) value = 0.330), which is relatively low compared to the R\({}^{2}\) values in regression analysis. It was found that jitter before and after treatment and MPT before treatment (\(p=0.034\), \(p=0.040\), \(p=0.024\)) significantly influence the effectiveness of voice therapy. In this paper, we propose new parameters for predicting the effectiveness of voice therapy by using the characteristics of acoustic parameters such as F0, jitter, shimmer, NHR, and the SFF comparison. Although the model using the proposed parameters did not show significant results in both males and females, there are plans to retest the utility of the suggested parameters using big data in the future. This paper emphasizes the significance \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Female** & **Male** \\ \hline
**Input Variable** & **Importance** & **Input Variable** & **Importance** \\ \hline Jitter (Post-tx) & 0.363 & MPT (Pre-tx) & 0.395 \\ Shimmer (Pre-tx) & 0.260 & Jitter (Post-tx) & 0.361 \\ Coffee-drinking status & 0.226 & Jitter (Pre-tx) & 0.244 \\ Jitter comparison & 0.151 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Importance of the input variables. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Female** & **Male** \\ \hline
**Performance Metrices** & **Values** & \\ \hline Accuracy (\%) & 87.5\% & 85.71\% \\ Precision & 0.92 & 1.00 \\ Specificity & 0.75 & 1.00 \\ Recall & 0.92 & 0.67 \\ G value & 0.83 & 0.82 \\ F score & 0.92 & 0.80 \\ AUC & 0.853 & 0.861 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Classification performance matrices of the MLP model. of introducing multiple parameters. Finally, the MLP model was evaluated to compare its performance, including accuracy, sensitivity, specificity, the F-score, and AUC.