A Study on Improving Sleep Apnea Diagnoses Using Machine Learning Based on the STOP-BANG Questionnaire

• Published in: Applied sciences Vol. 14; no. 7; p. 3117
• Main Authors: Choi, Myoung-Su, Han, Dong-Hun, Choi, Jun-Woo, Kang, Min-Soo
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2024
• Subjects: Analysis; Body mass index; Classification; diagnostic accuracy; Health aspects; Health care expenditures; Insomnia; Machine learning; Otolaryngology; Questionnaires; Regression analysis; Respiration; Risk factors; Sleep apnea; Sleep apnea syndromes; Sleep disorders; STOP-BANG; Variables; South Korea; Germany
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app14073117

Sleep apnea has emerged as a significant health issue in modern society, with self-diagnosis and effective management becoming increasingly important. Among the most renowned methods for self-diagnosis, the STOP-BANG questionnaire is widely recognized as a simple yet effective tool for diagnosing and assessing the risk of sleep apnea. However, its sensitivity and specificity have limitations, necessitating the need for tools with higher performance. Consequently, this study aimed to enhance the accuracy of sleep apnea diagnoses by integrating machine learning with the STOP-BANG questionnaire. Research through actual cases was conducted based on the data of 262 patients undergoing polysomnography, confirming sleep apnea with a STOP-BANG score of ≥3 and an Apnea–Hypopnea Index (AHI) of ≥5. The accuracy, sensitivity, and specificity were derived by comparing Apnea–Hypopnea Index scores with STOP-BANG scores. When applying machine learning models, four hyperparameter-tuned models were utilized: K-Nearest Neighbor (K-NN), Logistic Regression, Random Forest, and Support Vector Machine (SVM). Among them, the K-NN model with a K value of 11 demonstrated superior performance, achieving a sensitivity of 0.94, specificity of 0.85, and overall accuracy of 0.92. These results highlight the potential of combining traditional STOP-BANG diagnostic tools with machine learning technology, offering new directions for future research in self-diagnosis and the preliminary diagnosis of sleep-related disorders in clinical settings.