## 3 Materials and Methods
### Machine Learning Models  
In this study, four machine learning algorithms--random forest (RF), gradient boosting-based learning (GB), light gradient boosting machine (LightGBM), and extreme gradient boosting (XGBoost)--were applied to predict K-VRQOL and to identify its significant predictors. These algorithms were selected based on their ability to capture complex, nonlinear interactions among diverse features, including acoustic, clinical, and lifestyle-related variables. Unlike traditional statistical models, machine learning approaches offer greater flexibility and predictive accuracy, particularly in the presence of high-dimensional and heterogeneous data structures [33; 34; 35; 36]. RF is an ensemble learning algorithm based on the Bagging technique, where multiple decision trees are trained independently on random subsets of the data and features. The final prediction is obtained by aggregating the outputs of individual trees, either by majority vote (classification) or averaging (regression). This approach reduces variance and prevents overfitting, thereby enhancing model stability. Moreover, RF provides measures of feature importance, which aids in the interpretability of the model [33]. In this study, RF was employed to account for potential interactions among predictors and to identify the most influential variables associated with K-VRQOL. The GB algorithm is an ensemble learning technique that builds a strong predictive model by sequentially combining multiple weak learners, typically decision trees, while iteratively correcting residual errors using the gradient of the loss function. This approach enables the model to effectively capture complex nonlinear relationships and interactions among variables [34]. The algorithm was selected for its ability to efficiently handle complex and high-dimensional clinical data, accommodate missing values, and automatically identify nonlinear effects and variable interactions, which are common challenges in medical prediction studies. LightGBM is a highly efficient gradient-boosting framework developed by Microsoft, designed for speed and scalability. It adopts a leaf-wise tree growth strategy, where the leaf that results in the greatest loss reduction is split first. This results in deeper and more accurate trees compared with level-wise methods. LightGBM also supports histogram-based learning, categorical feature handling, automatic missing value imputation, and parallel processing, making it suitable for high-dimensional and large-scale datasets [35]. Given the mixed types of features (e.g., acoustic, categorical, and clinical) used in this study, LightGBM was chosen for its ability to deliver fast computation and a high predictive performance. XGBoost is an advanced implementation of gradient boosting algorithms. It incorporates L1 and L2 regularization to reduce overfitting, and supports several powerful features such as parallel training, early stopping, the automatic handling of missing values, and tree pruning. XGBoost is known for its superior predictive accuracy and has been widely adopted in data science competitions and real-world applications [36]. In this study, XGBoost was employed due to its robustness in handling complex feature interactions and its reliable performance even in relatively small datasets. Although each algorithm differs in their architecture and learning strategy, they all provide strong predictive capabilities and interpretability suited to the objectives of this study. RF offers model stability and feature importance analysis; LightGBM ensures computational efficiency and handles high-dimensional data effectively; XGBoost supports fine-tuned model regularization and complex interaction modeling; and GB underpins the optimization process in boosting algorithms. By applying and comparing these four algorithms, this study aimed to identify the most effective machine learning approach for predicting K-VRQOL while balancing accuracy, interpretability, and computational efficiency.