## 1 Introduction  
In modern society, an individual's quality of life (QOL) is determined not merely by survival or the presence of disease, but by a wide range of multidimensional factors such as psychological well-being, the quality of social relationships, job satisfaction, self-efficacy, and communication ability [1, 2, 3, 4]. Among these, the voice plays a central role in human interaction as a primary means of communication. It is essential for emotional expression, information exchange, and social engagement. In particular, for individualswhose professions depend heavily on vocal use--such as teachers, broadcasters, counselors, and call center agents--vocal health is critically important. When vocal problems occur, they can result in more than inconvenience; they may lead to serious limitations in occupational functioning, emotional distress, and social withdrawal [5; 6; 7; 8]. Due to these impacts, vocal health is increasingly being recognized not merely as a subcomponent of physical health, but as a crucial determinant of overall quality of life. Accordingly, there has been growing interest in the development and application of tools that can quantitatively assess the relationship between voice-related issues and QOL. One of the most widely adopted instruments for this purpose is the voice-related quality of life (VQOL) questionnaire, which measures subjective impairments and quality of life reduction caused by vocal dysfunction. This tool has been translated and culturally adapted for use in many countries [9; 10]. In Korea, the Korean version of the voice-related quality of life (K-VRQOL) instrument was developed to reflect its linguistic and cultural contexts. It has been validated for use in clinical and research settings and is employed for the diagnosis of voice disorders, assessment of pre- and post-therapy outcomes, and monitoring of patients' quality of life. Its standardized and user-friendly design makes it an effective tool for capturing patients' subjective experiences and for guiding patient-centered care [11; 12]. Despite its utility, however, empirical research on the determinants of K-VRQOL scores remains limited. Prior studies have largely focused on individual demographic or medical factors, such as sex, age, smoking status, alcohol consumption, presence of comorbidities, or history of surgery, and have often relied on small sample sizes and traditional univariate or multivariate statistical analyses [13; 14]. Furthermore, objective acoustic parameters such as jitter, shimmer, and the speaking fundamental frequency (SFF), which provide valuable insight into vocal function, are frequently excluded due to the complexity of data collection and interpretation [15; 16]. Additionally, conventional statistical methods have inherent limitations in modeling the complex and nonlinear interactions between predictors. For instance, the effects of smoking or vocal training on QOL may vary depending on individual conditions, yet these conditional or interaction effects are difficult to detect using traditional approaches [17; 18]. To address such limitations, the application of artificial intelligence (AI) and machine learning (ML) techniques in health-related research has gained traction. These methods are capable of learning complex, nonlinear patterns in data and provide not only accurate predictions but also meaningful insights into the relative importance of various predictors [19]. In particular, explainable AI techniques such as Shapley additive explanations (SHAP) allow researchers and clinicians to understand how each variable contributes to model predictions in a transparent and interpretable manner. SHAP has emerged as a powerful tool for enhancing trust and understanding in AI-driven health research [17; 20; 21]. Nevertheless, studies that employ machine learning-based integrated analyses to predict K-VRQOL scores--and explain the results in clinically interpretable ways--are still scarce, both in Korea and internationally.