머신러닝을 활용한 통계 분석 기반의 수면 호흡 장애 중증도 예측
Severity Prediction of Sleep Respiratory Disease Based on Statistical Analysis Using Machine Learning
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Currently, polysomnography is essential to diagnose sleep-related breathing disorders. However, there are several disadvantages to polysomnography, such as the requirement for multiple sensors and a long reading time. In this paper, we propose a system for predicting the severity of sleep-related breathing disorders at home utilizing measurable elements in a wearable device. To predict severity, the variables were refined through a three-step variable selection process, and the refined variables were used as inputs into three machine-learning models. As a result of the study, random forest models showed excellent prediction performance throughout. The best performance of the model in terms of F1 scores for the three threshold criteria of 5, 15, and 30 classified as the AHI index was about 87.3%, 90.7%, and 90.8%, respectively, and the maximum performance of the model for the three threshold criteria classified as the RDI index was approx 79.8%, 90.2%, and 90.1%, respectively.
Abstract
Currently, polysomnography is essential to diagnose sleep-related breathing disorders. However, there are several disadvantages to polysomnography, such as the requirement for multiple sensors and a long reading time. In this paper, we propose a system for predicting the severity of sleep-related breathing disorders at home utilizing measurable elements in a wearable device. To predict severity, the variables were refined through a three-step variable selection process, and the refined variables were used as inputs into three machine-learning models. As a result of the study, random forest models showed excellent prediction performance throughout. The best performance of the model in terms of F1 scores for the three threshold criteria of 5, 15, and 30 classified as the AHI index was about 87.3%, 90.7%, and 90.8%, respectively, and the maximum performance of the model for the three threshold criteria classified as the RDI index was approx 79.8%, 90.2%, and 90.1%, respectively.
참고문헌 (22)
- J. K. Kim, "Prevalence of Sleep-disordered Breathing in Middle-aged Korean Men and Women", American Journal of Respiratory and Critical Care Medicine, 170(10), pp.1108~1113 (2004)
- M. Arzt, "Association of Sleep-disordered Breathing and the Occurrence of Stroke", American Journal of Respiratory and Critical Care Medicine, 172(11), pp.1447~1451 (2005)
- H. K. Yaggi, "Obstructive Sleep Apnea as a Risk Factor for Stroke and Death", New England Journal of Medicine, 353(19), pp.2034~2041 (2005)
- 최수전, "폐쇄성 수면 무호흡의 합병증", 대한내과학회지, 89(1), pp.13~20 (2015), 대한내과학회[DOI]
- J. Teran, "The Association Between Sleep Apnea and the Risk of Traffic Accidents", New England Journal of Medicine, 340(11), pp.847~851 (1999)
- P. S. Jeong, "Development of Non-contact Sleep-disordered Breathing Detection System using Air-Mattress", Master, pp.2016
- P. S. Jeong, "Development of Non-contact Sleep-disordered Breathing Detection System using Air-Mattress", Master, pp.2016
- S. F. Quan, "Sleep-related Breathing Disorders in Adults:Recommendations for Syndrome Definition and Measurement Techniques in Clinical Research", Editorials. Sleep (New York, NY), 22(5), pp.662~689 (1999)
- 김라멕, "태양광 발전량 예측을 위한 랜덤포레스트와 순환신경망 비교", 대한환경공학회지, 43(5), pp.347~356 (2021), 대한환경공학회[DOI]
- 홍기혜, "랜덤포레스트 머신러닝 알고리즘 기반 남·여 청소년의 자살생각 예측 및 분석", 한국사회복지학, 72(3), pp.157~180 (2020), 한국사회복지학회[DOI]
- B. S. Seo, "A Study on the Prediction Model of the Elderly Depression Based on Random Forest", Master, pp.2019
- B. S. Seo, "A Study on Prediction of Excessive Outpatient Care Users Using Random Forest and Artificial Neural Network", Master, pp.2019
- 오윤주, "Hand-crafted 특징 및 머신 러닝 기반의 은하 이미지 분류 기법 개발", 대한임베디드공학회논문지, 16(1), pp.17~27 (2021), 대한임베디드공학회[DOI]
- 한지형, "머신러닝을 통한 가계의 재무스트레스 영향요인 예측 및 분석: XGBoost의 활용", 소비자학연구, 30(2), pp.21~43 (2019), 한국소비자학회[DOI]
- J. H. Kim, "Hybrid AdaBoost for Multiuser Priority Analysis", Master, pp.2016
- J. H. Kim, "A Comparative Analysis of Intelligent Classification Algorithms for Early Diabetes Prediction", Master, pp.2021
- J. H. Kim, "Comparative Analysis of Corporate Distress Predictive Models Using XGBoost", Master, pp.2019
- T. Chen, "Xgboost: A Scalable Tree Boosting System", Proceedings of the 22nd acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp.785~794 (2016)
- J. H. Friedman, "Greedy Function Approximation: a Gradient Boosting Machine", Annals of Statistics, pp.1189~1232 (2001)
- C. J. Kim, "Price Prediction on Building Prices with XGBoost Algorithm : Focusing on Eco-friendly Certification System", Master, pp.2022
- "Degradation State Recognition of Piston Pump Based on ICEEMDAN and XGBoost - Scientific Figure on ResearchGate"[URL]
- 김동길, "텐서플로우 튜토리얼 방식의 머신러닝 신규 모델 개발 : 캐글 타이타닉 데이터 셋을 중심으로", 대한임베디드공학회논문지, 14(4), pp.207~218 (2019), 대한임베디드공학회[DOI]