The Prediction of Obstructive Sleep Apnea Using Data Mining Approaches

Background: Obstructive sleep apnea (OSA) which is the most common sleep disorder breathing (SDB), imposes heavy costs on health and economy. The aim of this study was to provide models based on data mining approaches (C5.0 decision tree and logistic regression model [LRM]) and choose a top model for predicting OSA without polysomnography (PSG) devices that is a standard method for diagnosis of this disease, to identify patients with this syndrome payment. Methods: In this cross sectional study, data was extracted from the medical records of 333 patients with sleep disorders who were referred to sleep disorders research center of Kermanshah University of Medical Sciences during the years 2012–2016. All patients underwent one night PSG. A stepwise LRM was fitted and its performance was compared with C5.0 decision tree with use of the criteria of accuracy, sensitivity and specificity. Results: For C5.0 decision tree, accuracy was obtained 0.757 with 95% confidence interval (0.661, 0.838), sensitivity was 0.66 and specificity was 0.809. For LRM, these items were obtained 0.737 (0.639, 0.820), 0.693 and 0.78 respectively. Conclusion: C5.0 decision tree showed better performance than LRM in diagnosis of OSA. So this model can be considered as an alternative approach for PSG.