## 4 Discussion  
In this study, we employed binomial logistic regression analysis [29; 30], random forest (RF) [31; 32; 33], and the MLP model [38; 39; 40; 41] to predict the effectiveness of voice treatment based on various personal habits and clinical variables. The primary objective of this study was to analyze differences in VHI scores before and after therapy and identify key factors influencing the success of voice treatment. The binomial logistic regression analysis revealed that gender was a significant predictor of improvements in VHI scores; specifically, female patients showed greater improvement in VHI scores after voice therapy compared to male patients, a finding suggesting that gender differences may play a crucial role in how patients perceive and respond to voice therapy. Clinically, this result indicates the need for tailored therapeutic approaches that consider gender-specific responses. For instance, more aggressive treatment protocols or gender-specific interventions may be beneficial for female patients to maximize the effectiveness of voice therapy. On the other hand, variables such as smoking status, alcohol consumption, and voice use status did not show statistically significant effects on VHI scores, which suggests that these factors may not directly influence the outcome of voice therapy. However, these results are based on a surface-level analysis, and future research should explore potential indirect effects of, or interactions among, these variables. For example, voice use status, when combined with certain environmental or lifestyle factors, might have a more substantial impact on the effectiveness of voice therapy. The MLP model analysis identified gender, surgical history, voice use status, and voice training status as important predictors of voice therapy outcomes. Notably, surgical history and voice use status were shown to have high importance in the model, indicating that the physical causes of voice disorders and voice usage patterns significantly influence therapy outcomes. For instance, patients with a history of surgery might experience more significant changes in their voice condition following treatment; additionally, individuals who regularly use their voice may respond more sensitively to therapy, likely due to the \begin{table}
\begin{tabular}{c c} \hline \hline
**Performance** & **Value** \\ \hline Accuracy (\%) & 84.61\% \\ Precision & 0.95 \\ Specificity & 0.67 \\ Recall & 0.86 \\ F score & 0.90 \\ AUC & 0.87 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Classification performance of MLP model. Figure 7: The relative importance of each variable. higher impact of voice on their quality of life. Voice training status also emerged as a key variable, suggesting that regular voice training contributes positively to the effectiveness of voice therapy, a finding that underscores the importance of continuous voice exercises in maintaining and enhancing vocal function. Clinically, this supports the recommendation for ongoing voice training as part of the therapeutic regimen, and further research should investigate the frequency and intensity of training needed to optimize treatment outcomes. The performance evaluation of the RF and MLP models reveals notable strengths and weaknesses for each approach. The MLP model outperforms the RF model in terms of precision and specificity, suggesting that the MLP model is more effective at minimizing false-positive predictions and accurately classifying negative cases. In contrast, the RF model exhibits higher recall, indicating its ability to identify a greater number of positive cases. However, the RF model's specificity of 0.00 represents a significant drawback, as it fails to accurately classify negative cases. In conclusion, the MLP model provides a more balanced performance overall, particularly excelling in the classification of negative cases compared to the RF model.