## 5 Discussion  
This study evaluated changes in K-VRQOL before and after voice therapy and analyzed the personal, clinical, and acoustic predictors contributing to these changes using both multiple regression and machine learning-based predictive models. In addition to traditional statistical approaches, the integration of machine learning and SHAP interpretation techniques enhanced the interpretability and visualization of the predictive contribution of each variable. The multiple regression analysis [30; 31; 32] identified gender and comorbidity status as consistent and significant predictors of improvement in both the total and physical domains. Specifically, female patients and those with comorbid conditions showed greater improvement in their K-VRQOL scores. These results are in line with previous findings, suggesting that differences in vocal sensitivity by gender and a higher baseline discomfort among those with comorbidities may influence patients' perception of therapy gains. This underscores the importance of personalized counseling and intervention strategies that consider individual background characteristics. Among the acoustic variables, jitter, SFF, and MPT showed significant associations, particularly in the physical domain. These findings indicate that vocal periodicity, stability, and phonatory endurance are closely linked to the physical recovery of voice function after therapy. Specifically, jitter and SFF reflect fine frequency perturbation and spectral flatness, respectively, and their reduction implies improved vocal clarity and stability. Thus, these acoustic features may serve as sensitive biomarkers for tracking therapeutic outcomes. In contrast, the emotional domain demonstrated relatively  
Figure 4: SHAP summary plots for parameter contributions in predicting K-VRQOL outcomes across (**a**) total, (**b**) physical, and (**c**) emotional domains. lower explanatory power and predictive accuracy. Most predictors did not reach statistical significance, and model performance remained limited. Interestingly, smoking status was a significant predictor of emotional improvement. This may suggest that smokers have a lower baseline quality of life or different expectations, leading to a higher perceived benefit post-therapy. However, the underlying mechanisms warrant further investigation. The low predictive power in the emotional domain implies that psychosocial factors not captured by quantitative variables may play a larger role, highlighting the need for future models incorporating qualitative and psychosocial variables such as stress, social support, and prior counseling experience. Machine learning approaches [33; 34; 35; 36] demonstrated strengths in capturing nonlinear patterns not accounted for by regression models. Notably, LightGBM achieved the highest explanatory power for the total score (32.54%) and demonstrated balanced performance in the physical and emotional domains. This model's flexibility--through sampling, feature selection, regularization, and complexity control--may have contributed to its strong performance. The gradient boosting model performed best in the physical domain (30.50%), suggesting that structural improvements in vocal function were effectively captured. Interestingly, random forest, despite its simplicity, showed a relatively high performance in the emotional domain (17.33%), possibly due to its robustness in identifying complex feature interactions without overfitting. As shown in Figure 3, a relatively low performance may be attributed to the experimental design, which would require a precise prediction of continuous K-VRQOL scores. In other words, a few extreme values in the K-VRQOL distribution disproportionately influenced the overall model performance. It is anticipated that reframing the prediction task as a classification problem--by categorizing the degree of improvement into two or three discrete classes--could yield more robust and interpretable results. SHAP analysis enhanced the interpretability of the model predictions.