Can Statistical Machine Learning Algorithms Help for Classification of Obstructive Sleep Apnea Severity to Optimal Utilization of Polysomno graphy Resources?^[*]

 

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Summary

Objectives: The goal of this study is to evaluate the results of machine learning methods for the classification of OSA severity of patients with suspected sleep disorder breathing as normal, mild, moderate and severe based on non-polysomnographic variables: 1) clinical data, 2) symptoms and 3) physical examination.

Methods: In order to produce classification models for OSA severity, five different machine learning methods (Bayesian network, Decision Tree, Random Forest, Neural Networks and Logistic Regression) were trained while relevant variables and their relationships were derived empirically from observed data. Each model was trained and evaluated using 10-fold cross-validation and to evaluate classification performances of all methods, true positive rate (TPR), false positive rate (FPR), Positive Predictive Value (PPV), F measure and Area Under Receiver Operating Characteristics curve (ROC-AUC) were used.

Results: Results of 10-fold cross validated tests with different variable settings promisingly indicated that the OSA severity of suspected OSA patients can be classified, using non-polysomnographic features, with 0.71 true positive rate as the highest and, 0.15 false positive rate as the lowest, respectively. Moreover, the test results of different variables settings revealed that the accuracy of the classification models was significantly improved when physical examination variables were added to the model.

Conclusions: Study results showed that machine learning methods can be used to estimate the probabilities of no, mild, moderate, and severe obstructive sleep apnea and such approaches may improve accurate initial OSA screening and help referring only the suspected moderate or severe OSA patients to sleep laboratories for the expensive tests.

^* Supplementary material published on our website https://doi.org/10.3414/ME16-01-0084

• References

• 1 Punjabi NM. The epidemiology of adult obstructive sleep apnea. Proceedings of the American Thoracic Society 2008; 5: 136-143.
  CrossrefPubMedGoogle Scholar
• 2 Young T, Peppard PE, Gottlieb DJ. Epidemiology of obstructive sleep apnea: a population health perspective. Am J Respir Crit Care Med 2002; 165: 1217-1239.
  CrossrefPubMedGoogle Scholar
• 3 Berry RB, Budhiraja R, Gottlieb DJ. et al. Rules for scoring respiratory events in sleep: update of the 2007 AASM Manual for the Scoring of Sleep and Associated Events. Deliberations of the Sleep Apnea Definitions Task Force of the American Academy of Sleep Medicine. J Clin Sleep Med 2012; 8: 597-619.
  CrossrefPubMedGoogle Scholar
• 4 Tang B, Yan W. A Novel Model Using Generalized Regression Neural Network (Grnn) For Estimating Sleep Apnea Index in The Elderly Suffering from Sleep Disturbance. Int J Med Biol Front 2011; 17: 313.
  PubMedGoogle Scholar
• 5 Ustun B, Westover M, Rudin C, Bianchi M. Clinical Prediction Models for Sleep Apnea: The Importance of Medical History over Symptoms. J Clin Sleep Med: JCSM 2016; 12 (02) 161-168.
  PubMedGoogle Scholar
• 6 Bouloukaki I, Kapsimalis F, Mermigkis C. et al. Prediction of obstructive sleep apnea syndrome in a large Greek population. Sleep Breath 2011; 15: 657-664.
  CrossrefPubMedGoogle Scholar
• 7 Eiseman NA, Westover MB, Mietus JE, Thomas RJ, Bianchi MT. Classification algorithms for predicting sleepiness and sleep apnea severity. J Sleep Res 2012; 21: 101-112.
  CrossrefPubMedGoogle Scholar
• 8 Ghandeharioun H, Rezaeitalab F, Lotfi R. Accurate Estimation of Obstructive Sleep Apnea Severity Using Non-Polysomnographic Features for Home- Based Screening. Iran J Public Health 2015; 44: 1433-1435.
  PubMedGoogle Scholar
• 9 Kirby SD, Eng P, Danter W. et al. Neural network prediction of obstructive sleep apnea from clinical criteria. Chest 1999; 116: 409-415.
  CrossrefPubMedGoogle Scholar
• 10 Musman S, Passos VM DA, Silva IB R, Barreto SM. Evaluation of a prediction model for sleep apnea in patients submitted to polysomnography. J Bras Pneumol 2011; 37: 75-84.
  CrossrefPubMedGoogle Scholar
• 11 Karamanli H, Yalcinoz T, Yalcinoz MA, Yalcinoz T. A prediction model based on artificial neural networks for the diagnosis of obstructive sleep apnea. Sleep Breath. 2015: 1-6.
  PubMedGoogle Scholar
• 12 Pang Z, Liu D, Lloyd SR. Classification of Obstructive Sleep Apnea by Neural Networks. 4th International Symposium on Neural Networks. 2007: 1299-1308.
  PubMedGoogle Scholar
• 13 Sun LM, Chiu HW, Chuang CY, Liu L. A prediction model based on an artificial intelligence system for moderate to severe obstructive sleep apnea. Sleep Breath 2011; 15: 317-323.
  CrossrefPubMedGoogle Scholar
• 14 Teferra RA, Grant BJ, Mindel JW. et al. Cost minimization using an artificial neural network sleep apnea prediction tool for sleep studies. Ann Am Thorac Soc 2014; 11: 1064-1074.
  CrossrefPubMedGoogle Scholar
• 15 Farney RJ, Walker BS, Farney RM, Snow GL, Walker JM. The STOP-Bang equivalent model and prediction of severity of obstructive sleep apnea: relation to polysomnographic measurements of the apnea/hypopnea index. J Clin Sleep Med 2011; 7: 459-465B.
  CrossrefPubMedGoogle Scholar
• 16 Ting H, Mai YT, Hsu HC, Wu HC, Tseng MH. Decision tree based diagnostic system for moderate to severe obstructive sleep apnea. J Med Syst 2014; 38: 94.
  CrossrefPubMedGoogle Scholar
• 17 Flemons WW, Mcnicholas WT. Clinical prediction of the sleep apnea syndrome. Sleep medicine reviews 1997; 1: 19-32.
  CrossrefPubMedGoogle Scholar
• 18 Tsai WH, Remmers JE, Brant R, Flemons WW, Davies J, Macarthur C. A decision rule for diagnostic testing in obstructive sleep apnea. Am. J. Respir. Crit. Care Med 2003; 167: 1427-1432.
  CrossrefPubMedGoogle Scholar
• 19 Zonato AI, Bittencourt LR, Martinho FL, Junior JF, Gregorio LC, Tufik S. Association of systematic head and neck physical examination with severity of obstructive sleep apnea-hypopnea syndrome. Laryngoscope 2003; 113: 973-980.
  CrossrefPubMedGoogle Scholar
• 20 Nuckton TJ, Glidden DV, Browner WS, Claman DM. Physical examination: Mallampati score as an independent predictor of obstructive sleep apnea. Sleep 2006; 29: 903-908.
  CrossrefPubMedGoogle Scholar
• 21 Sahin M, Bilgen C, Tasbakan MS, Midilli R, Basoglu OK. A clinical prediction formula for apnea-hypopnea index. Int J Otolaryngol. 2014: 438376.
  PubMedGoogle Scholar
• 22 Pereira Rodrigues P, Ferreira Santos D, Leite L. Obstructive Sleep Apnea Diagnosis: The Bayesian Network Model Revisited. Computer-Based Medical Systems (CBMS), 2015 IEEE 28th International Symposium on.. IEEE; 2015: 115-120.
  Google Scholar
• 23 World Health Organization. BMI classification. Global database on body mass index. 2006 Available from: http://apps.who.int/bmi/index.jsp?in-troPage=intro_3.html.
  PubMedGoogle Scholar
• 24 Petry NM. A comparison of young, middle-aged, and older adult treatment-seeking pathological gamblers. The Gerontologist 2002; 42: 92-99.
  CrossrefPubMedGoogle Scholar
• 25 Johns MW. A new method for measuring daytime sleepiness: the Epworth sleepiness scale. Sleep 1991; 14: 540-545.
  CrossrefPubMedGoogle Scholar
• 26 Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH. The WEKA data mining software: an update. ACM SIGKDD explorations newsletter 2009; 11: 10-18.
  CrossrefPubMedGoogle Scholar
• 27 Friedman N, Geiger D, Goldszmidt M. Bayesian network classifiers. Machine Learning 1997; 29: 131.
  CrossrefPubMedGoogle Scholar
• 28 Kohonen T. Learning vector quantization. The Handbook of Brain Theory and Neural Networks.. Cambridge: MIT Press; 1995: 537-540.
  Google Scholar
• 29 Norsys Software Corp. 1992-2016, Netica version 5.04 [cited 2016 March]. Available from: www.norsys.com.
  PubMed
• 30 Huang J, Ling CZ. Using AUC and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 2005; 17: 299-310.
  CrossrefPubMedGoogle Scholar
• 31 Delong ER, Delong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 1988; 44: 837-845.
  CrossrefPubMedGoogle Scholar
• 32 Hastie T, Tibshirani R, Friedman JH. The elements of statistical learning: data mining, inference, and prediction. New York: Springer; 2009
  Google Scholar
• 33 Ferri C, Hernández-Orallo J, Modroiu R. An experimental comparison of performance measures for classification. Pattern Recognition Letters 2009; 30 (01) 27-38.
  CrossrefPubMedGoogle Scholar
• 34 Montoya FS, Bedialauneta JRI, Larracoechea UA, Ibargüen AM, Del Rey AS, Fernandez JMS. The predictive value of clinical and epidemiological parameters in the identification of patients with obstructive sleep apnoea (OSA): a clinical prediction algorithm in the evaluation of OSA. Eur Arch Otorhinolaryngol 2007; 264: 637-643.
  CrossrefPubMedGoogle Scholar
• 35 Alan J, Brkic K, Bogunovic N. A review of feature selection methods with applications. Information and Communication Technology, Electronics and Microelectronics (MIPRO), 2015 38th International Convention on.. IEEE; 2015
  Google Scholar
• 36 Guyon I, André E. An introduction to variable and feature selection. Journal of Machine Learning Research. 2003: 1157-1182.
  PubMedGoogle Scholar
• 37 Wang D, Liangxiao J. An improved attribute selection measure for decision tree induction. Fuzzy Systems and Knowledge Discovery, 2007. FSKD 2007. Fourth International Conference on.. 4 IEEE 2007
  PubMedGoogle Scholar
• 38 Bianchi MT. Screening for obstructive sleep apnea: Bayes weighs in. Open Sleep J. 2009: 56-59.
  PubMedGoogle Scholar
• 39 Mirrakhimov AE, Sooronbaev T, Mirrakhimov EM. Prevalence of obstructive sleep apnea in Asian adults: a systematic review of the literature. BMC Pulm Med 2013; 13: 10.
  CrossrefPubMedGoogle Scholar

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