다양한 머신 러닝 알고리즘을 이용한 폐쇄수면무호흡 진단의 정확도 = Diagnostic Accuracy of Different Machine Learning Algorithms for Obstructive Sleep Apnea

다국어 초록 (Multilingual Abstract)

Objectives: The objective of this study was to develop models for predicting obstructive sleep apnea (OSA) based on easily obtainable clinical information of patients using various machine learning techniques.
Methods: We used a data set that included the records of 1,368 patients, in which 1,074 patients were male (78.5 %), and 294 patients were female (21.5 %). We randomly divided the data into a training set (1,000) and test set (368). Five machine learning methods, i.e., support vector machine model, lasso logit model, naïve bayes, discriminant analysis, and K-nearest neighbor (KNN), with a 10-cross fold technique were used with the proposed model to predict OSA. We evaluated the accuracy, sensitivity, specificity, and precision of each model for three thresholds [Apnea-Hypopnea Index (AHI)≥5, AHI≥15, and AHI≥30]. Results: Among the machine learning techniques, KNN showed the best results compared to the other techniques. The accuracy, sensitivity, specificity, and precision of OSA prediction were 87.0%, 91.0%, 74.4%, and 91.9%, respectively, based on AHI≥5. When the threshold of OSA was AHI≥15 or AHI≥30, KNN provided lower accuracy (79.6% each) and precision (79.0% and 68.7%), which were still higher than those of the other techniques. Conclusions: The model derived from the KNN technique exhibited the best performance based on its highest level of accuracy. We demonstrate that this model is a useful tool for predicting OSA.

참고문헌 (Reference)

1 양현주, "폐쇄성수면무호흡 진단을 위한 Berlin, STOP, STOP-Bang 설문의 유용성에 관한 고찰" 대한수면연구학회 16 (16): 11-20, 2019

2 양요찬, "서울지역 대학생들의 요천추 만곡과 목-허리둘레의 관계에 대한 연구" 한방재활의학과학회 23 (23): 169-176, 2013

3 Yu JL, "Utility of the modified Mallampati grade and Friedman tongue position in the assessment of obstructive sleep apnea" 16 : 303-308, 2020

4 Friedman M, "Updated Friedman Staging System for obstructive sleep apnea" 80 : 41-48, 2017

5 Kapur V, "Underdiagnosis of sleep apnea syndrome in U.S. communities" 6 : 49-54, 2002

6 Rowley JA, "The use of clinical prediction formulas in the evaluation of obstructive sleep apnea" 23 : 929-938, 2000

7 Kapur V, "The medical cost of undiagnosed sleep apnea" 22 : 749-755, 1999

8 Hastie T, "The elements of statistical learning:data mining, inference, and prediction" Springer 2009

9 Farney RJ, "The STOPBang equivalent model and prediction of severity of obstructive sleep apnea : relation to polysomnographic measurements of the apnea/hypopnea index" 7 : 459-465, 2011

10 Khandoker AH, "Support vector machines for automated recognition of obstructive sleep apnea syndrome from ECG recordings" 13 : 37-48, 2009

11 American Academy of Sleep Medicine Task Force, "Sleep-related breathing disorders in adults: recommendations for syndrome definition and measurement techniques in clinical research. The Report of an American Academy of Sleep Medicine Task Force" 22 : 667-689, 1999

12 Shahar E, "Sleep-disordered breathing and cardiovascular disease: cross-sectional results of the Sleep Heart Health Study" 163 : 19-25, 2001

13 Engleman HM, "Sleep · 4 : sleepiness, cognitive function, and quality of life in obstructive sleep apnoea/hypopnoea syndrome" 59 : 618-622, 2004

14 Yang KI, "Sleep disorders: case-based learning" PanMun Education 154-156, 2020

15 Sunwoo JS, "Prevalence, sleep characteristics, and comorbidities in a population at high risk for obstructive sleep apnea : a nationwide questionnaire study in South Korea" 13 : e0193549-, 2018

16 Mirrakhimov AE, "Prevalence of obstructive sleep apnea in Asian adults : a systematic review of the literature" 13 : 10-, 2013

17 Liu WT, "Prediction of the severity of obstructive sleep apnea by anthropometric features via support vector machine" 12 : e0176991-, 2017

18 Costa LE, "Potential underdiagnosis of obstructive sleep apnoea in the cardiology outpatient setting" 101 : 1288-1292, 2015

19 Nuckton TJ, "Physical examination : Mallampati score as an independent predictor of obstructive sleep apnea" 29 : 903-908, 2006

20 Malhotra A, "Obstructive sleep apnoea" 360 : 237-245, 2002

21 Li KK, "Obstructive sleep apnea syndrome: a comparison between Far-East Asian and white men" 110 (110): 1689-1693, 2000

22 Keshavarz Z, "Obstructive sleep apnea : a prediction model using supervised machine learning method" 272 : 387-390, 2020

23 Samant P, "Machine learning techniques for medical diagnosis of diabetes using iris images" 157 : 121-128, 2018

24 Rajkomar A, "Machine learning in medicine" 380 : 1347-1358, 2019

25 Sateia MJ, "International classification of sleep disorders-third edition" 146 : 1387-1394, 2014

26 Peppard PE, "Increased prevalence of sleep-disordered breathing in adults" 177 : 1006-1014, 2013

27 Cameron N, "Human growth: its assessment, evaluation and variation" Loughborough University 2018

28 Katz SL, "Does neck-to-waist ratio predict obstructive sleep apnea in children" 10 : 1303-1308, 2014

29 Friedman M, "Diagnostic value of the Friedman tongue position and Mallampati classification for obstructive sleep apnea : a meta-analysis" 148 : 540-547, 2013

30 Deflandre E, "Development and validation of a morphologic obstructive sleep apnea prediction score : the DES-OSA score" 122 : 363-372, 2016

31 Islam SMS, "Deep learning of facial depth maps for obstructive sleep apnea prediction" IEEE 154-157, 2018

32 Chekroud AM, "Cross-trial prediction of treatment outcome in depression : a machine learning approach" 3 : 243-250, 2016

33 Aaronson JA, "Can a prediction model combining self-reported symptoms, sociodemographic and clinical features serve as a reliable first screening method for sleep apnea syndrome in patients with stroke?" 95 : 747-752, 2014

34 Young T, "Burden of sleep apnea: rationale, design, and major findings of the Wisconsin Sleep Cohort study" 108 : 246-249, 2009

35 Oğretmenoğlu O, "Body fat composition : a predictive factor for obstructive sleep apnea" 115 : 1493-1498, 2005

36 Uçar MK, "Automatic sleep staging in obstructive sleep apnea patients using photoplethysmography, heart rate variability signal and machine learning techniques" 29 : 1-16, 2018

37 Waxman JA, "Automated prediction of apnea and hypopnea, using a LAMSTAR artificial neural network" 181 : 727-733, 2010

38 Nieto FJ, "Association of sleep-disordered breathing, sleep apnea, and hypertension in a large community-based study" 283 : 1829-1836, 2000

39 Pépin JL, "Assessment of mandibular movement monitoring with machine learning analysis for the diagnosis of obstructive sleep apnea" 3 : e1919657-, 2020

40 Mencar C, "Application of machine learning to predict obstructive sleep apnea syndrome severity" 26 : 298-317, 2020

41 Wysocki J, "Anthropometric and physiologic assessment in sleep apnoea patients regarding body fat distribution" 75 : 393-399, 2016

42 James G, "An introduction to statistical learning with applications in R" Springer 2013

43 Gunčar G, "An application of machine learning to haematological diagnosis" 8 : 411-, 2018

44 Wu MF, "A new method for self-estimation of the severity of obstructive sleep apnea using easily available measurements and neural fuzzy evaluation system" 21 : 1524-1532, 2017