## 4 Results
### Performance Evaluation of Machine Learning Models  
In this study, four machine learning regression models--RF, GB, LightGBM, and XGBoost--were employed to predict therapy-induced changes in K-VRQOL scores. Model performance was assessed through five-fold cross-validation to ensure generalizability and robustness. Each model was trained using predefined hyperparameter settings (Table 5), and their predictive performances were evaluated across the total, physical, and emotional domains (Table 6). LightGBM achieved the highest predictive accuracy in the total domain (32.54%), demonstrating the effectiveness of its balanced configuration, which included a shallow tree depth (max_depth = 3), a limited number of leaves (num_leaves = 5), and regularization techniques (reg_alpha = 0.5, reg_lambda = 1.0). Subsampling strategies (subsample = 0.8, colsample_bytree = 0.6) further enhanced its generalization ability. The model also exhibited consistent performance in the physical (30.20%) and emotional (15.45%) domains, indicating its robustness across multiple dimensions of the K-VRQOL. The gradient boosting model (GB), trained with a large number of estimators (n_estimators = 800) and anti-overfitting constraints (min_samples_leaf = 3, max_features ='sqrt'), showed the best performance in the physical domain (30.50%). This result suggests that the model effectively captured the physical improvements derived from voice therapy. However, its performance in the emotional domain was considerably lower (6.81%), indicating a limited sensitivity to emotional variability. The RF, characterized by a very shallow tree depth (max_depth = 1) and a small number of trees (n_estimators = 60), was generally limited in capturing complex patterns. Nonetheless, it achieved the highest performance in the emotional domain (17.33%), suggesting that simpler models may still capture meaningful variance in affective outcomes. Its performance in the total (21.82%) and physical (24.11%) domains remained moderate. The XGBoost model, configured with a very low learning rate (learning_rate = 0.01) and shallow trees (max_depth = 2), prioritized stability and generalization through strong regularization (reg_alpha = 0.7, reg_lambda = 1.0). While this approach resulted in a lower performance in the total (17.50%) and emotional (11.74%) domains, it achieved a notable performance in the physical domain (27.83%), demonstrating domain-specific effectiveness. These findings highlight the importance of aligning model complexity and regularization strategies with the characteristics of each target domain. In the context of multidimen \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Total Domain** & **Physical Domain** & **Emotional Domain** \\ \hline GB & 20.91\% & **30.50\%** & 6.81\% \\ \hline RF & 21.82\% & 24.11\% & **17.33\%** \\ \hline LightGBM & **32.54\%** & 30.20\% & 15.45\% \\ \hline XGBoost & 17.50\% & 27.83\% & 11.74\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance of machine learning models. \begin{table}
\begin{tabular}{c c} \hline \hline
**random forest (RF)** & n\_estimators = 60, learning\_rate = 0.4, max\_depth = 1 \\ \hline
**gradient boosting (GB)** & n\_estimators = 800, min\_samples\_leaf = 3, max\_features = ‘sqrt’ \\ \hline
**light gradient boosting machine** & objective = ‘regression’, n\_estimators = 1000, learning\_rate = 0.25, \\
**(LightGBM)** & max\_depth = 3, num\_leaves = 5, subsample = 0.8, colsample\_bytree = 0.6, \\  & reg\_alpha = 0.5, reg\_lambda = 1.0 \\ \hline
**extreme gradient boosting (XGBoost)** & objective = ‘reg\_squarederror’, n\_estimators = 1000, \\  & max\_depth = 2, learning\_rate = 0.01, reg\_alpha = 0.7, reg\_lambda = 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Information related to machine learning models. sional constructs such as K-VRQOL, the selection and optimization of machine learning models should be carefully tailored to maximize domain-specific predictive performance.