## 6 Conclusions  
This study represents a significant advancement in the early diagnosis and management of obstructive sleep apnea (OSA), a condition affecting a vast global population and critically impacting patients' health and quality of life. Traditional diagnostic methods for OSA are often time-consuming and costly, leading to a considerable number of patients not receiving timely and appropriate diagnoses. To overcome this challenge, we propose an integrated approach that combines a machine learning model with the STOP-BANG questionnaire. This method represents a significant step forward in the early diagnosis and effective management of OSA. The results of our study indicate that utilizing a machine learning algorithm, specifically the K-Nearest Neighbor (K-NN) model, can significantly improve the accuracy, sensitivity, and specificity of OSA diagnoses made using the STOP-BANG questionnaire. These findings highlight the potential of machine learning technologies in the medical field, particularly for the early detection and categorization of diseases. The application of machine learning models like K-NN has the potential to enhance early diagnostic procedures for various medical conditions.  
The practical implications of this study can be viewed from two perspectives. Firstly, the combined use of the STOP-BANG questionnaire and a machine learning model offers an effective method to enhance the precision of OSA diagnostics while reducing associated time and costs. This approach is expected to improve the efficiency of medical services and patient satisfaction. Secondly, this integrated approach opens new horizons for employing artificial intelligence (AI) technology in the medical sector. The application of machine learning models in the early detection and treatment of various illnesses has the potential to revolutionize disease prevention and management. Further research should aim to expand the scope of this inquiry by exploring a broader range of machine learning models, including deep learning algorithms, which could provide more detailed insights into OSA diagnosis.  
In conclusion, our study demonstrates that integrating the STOP-BANG questionnaire with a machine learning model can have a positive impact on the early diagnosis and management of OSA. This suggests that the implementation of AI technology in the medical field can lead to beneficial outcomes, including reduced medical expenses, shorter diagnosis and treatment times, and improved patient quality of life. However, the journey towards optimizing the use of AI in medicine is ongoing. It requires the continuous exploration, validation, and adaptation of emerging technologies to meet the changing needs of patients and health care providers.  
M.-S.C. and D.-H.H.Writing--original draft, Data curation, and Software. J.-W.C.: Visualization. M.-S.K.: Conceptualization, Validation, Writing--review and editing, Project administration. M.-S.C.: Formal analysis, Funding acquisition. [PERSON] is the first author. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This paper was supported by Eulji University in 2023 (EJRG-23-16).  
**Institutional Review Board Statement:** This study was conducted in accordance with and approved by the Institutional Review Board of Eulji Medical Center (protocol code 2022-03-010-002) for studies involving humans.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy and ethical restrictions.  
**Conflicts of Interest:** The authors declare no conflicts of interest.