## 2 Related Works  
Several prior studies have explored the relationship between vocal disorders and voice-related quality of life (V-RQOL), utilizing various populations, evaluation tools, and analytical methods [1, 9, 10, 19, 22, 23, 24]. [PERSON] et al. [1] investigated the impact of temporomandibular disorder (TMD) on voice-related quality of life (V-RQOL) using a multidisciplinary assessment framework. Utilizing the Diagnostic Criteria for TMD (DC/TMD) Axis I, the authors examined correlations between V-RQOL scores and psychological as well as functional variables such as depression (measured using the Hospital Anxiety and Depression Scale, HADS) and verbal/emotional expression (measured using the Jaw Functional Limitation Scale, JFLS-VEE). Multiple linear regression analysis revealed that higher depression scores and greater limitations in emotional expression were significantly associated with a lower V-RQOL, independent of age, gender, or pain duration. These findings highlight the importance of psychological and articulatory factors in voice perception and suggest that voice problems in TMD patients may not be merely mechanical but also psychosocial in nature. This underscores the need for multidisciplinary therapeutic approaches. The present study builds on this by incorporating comorbidity and emotional variables alongside objective acoustic measures in a machine learning framework to evaluate voice-related quality of life. [PERSON] et al. [19] developed a machine learning model to predict the general health-related quality of life (QOL) among middle-aged South Korean adults using a large-scale, population-based cohort (N = 4048). They employed seven machine learning algorithms and addressed class imbalance using the synthetic minority oversampling technique (SMOTE). This study used the SF-12 survey to assess QOL, dividing it into physical (PCS), mental (MCS), and total QOL. Among the predictors, stress level and sleep quality consistently emerged as the most important factors across all outcome domains. The random forest algorithm achieved the best predictive performance, with an area under the curve (AUC) of up to 0.822. This research demonstrated the feasibility and effectiveness of applying machine learning for multidimensional QOL prediction. Although not specific to the voice, it reinforces the applicability of algorithmic approaches in modeling complex health-related quality indices. Our study extends this framework to the domain of voice-related QOL by incorporating both subjective assessments and objective vocal features within a similar machine learning paradigm. [PERSON] et al. [22] proposed a novel, non-invasive method for estimating respiratory quality of life (RQoL) using voice biomarkers. Using data from over 1900 participants in the Colive Voice project, the authors extracted sustained vowel phonation features and trained deep learning models to predict RQoL levels. They compared models based on clinical features alone, voice features alone, and a combination of the two, finding that multimodal models significantly outperformed unimodal ones (AUROC = 0.77). Their study demonstrated that acoustic signals derived from smartphones could reflect underlying respiratory health conditions such as asthma. This supports the growing trend toward digital phenotyping and personalized, scalable health monitoring. In the present study, this work inspires the integration of acoustic features such as jitter, shimmer, and SFF into models predicting K-VRQOL, thereby moving beyond self-reported measures toward a more objective, data-driven approach. [PERSON] et al. [10] conducted a longitudinal analysis of patients with subglottic stenosis (SGS), assessing changes in their V-RQOL scores following different surgical interventions. Analyzing 130 procedures across 61 patients, the authors found that the improvement in airway caliber post-surgery was significantly correlated with gains in V-RQOL (\(\beta\) = 1.5, \(p\) = 0.02), regardless of the type, frequency, or interval of therapy.