A Study on Improving Sleep Apnea Diagnoses Using Machine Learning Based on the STOP-BANG Questionnaire
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 an...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/7/3117 |
| _version_ | 1827286876598304768 |
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| author | Myoung-Su Choi Dong-Hun Han Jun-Woo Choi Min-Soo Kang |
| author_facet | Myoung-Su Choi Dong-Hun Han Jun-Woo Choi Min-Soo Kang |
| author_sort | Myoung-Su Choi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T10:48:28Z |
| format | Article |
| id | doaj.art-114459f13c884fcdac069ff710efcc10 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-04-24T10:48:28Z |
| publishDate | 2024-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-114459f13c884fcdac069ff710efcc102024-04-12T13:15:45ZengMDPI AGApplied Sciences2076-34172024-04-01147311710.3390/app14073117A Study on Improving Sleep Apnea Diagnoses Using Machine Learning Based on the STOP-BANG QuestionnaireMyoung-Su Choi0Dong-Hun Han1Jun-Woo Choi2Min-Soo Kang3Daejeon Eulji Medical Center, Department of Otolaryngology-Head and Neck Surgery, Eulji University School of Medicine, Daejeon 35233, Republic of KoreaDepartment of Medical Artificial Intelligence, Eulji University, Seongnam 13135, Republic of KoreaDepartment of Medical IT, Eulji University, Seongnam 13135, Republic of KoreaDepartment of Bigdata Medical Convergence, Eulji University, Seongnam 13135, Republic of KoreaSleep 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.https://www.mdpi.com/2076-3417/14/7/3117sleep apneaAISTOP-BANGmachine learningdiagnostic accuracy |
| spellingShingle | Myoung-Su Choi Dong-Hun Han Jun-Woo Choi Min-Soo Kang A Study on Improving Sleep Apnea Diagnoses Using Machine Learning Based on the STOP-BANG Questionnaire Applied Sciences sleep apnea AI STOP-BANG machine learning diagnostic accuracy |
| title | A Study on Improving Sleep Apnea Diagnoses Using Machine Learning Based on the STOP-BANG Questionnaire |
| title_full | A Study on Improving Sleep Apnea Diagnoses Using Machine Learning Based on the STOP-BANG Questionnaire |
| title_fullStr | A Study on Improving Sleep Apnea Diagnoses Using Machine Learning Based on the STOP-BANG Questionnaire |
| title_full_unstemmed | A Study on Improving Sleep Apnea Diagnoses Using Machine Learning Based on the STOP-BANG Questionnaire |
| title_short | A Study on Improving Sleep Apnea Diagnoses Using Machine Learning Based on the STOP-BANG Questionnaire |
| title_sort | study on improving sleep apnea diagnoses using machine learning based on the stop bang questionnaire |
| topic | sleep apnea AI STOP-BANG machine learning diagnostic accuracy |
| url | https://www.mdpi.com/2076-3417/14/7/3117 |
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