Comparison of support vector machine based on genetic algorithm with logistic regression to diagnose obstructive sleep apnea

Abstract

Background: Diagnosing of obstructive sleep apnea (OSA) is an important subject in medicine. Tis study aimed to compare the performance of two data mining techniques, support vector machine (SVM), and logistic regression (LR), in diagnosing OSA. Te best?ft model was used as a substitute for polysomnography (PSG), which is the gold standard for diagnosing this disease. Materials and Methods: A total of 250 patients with sleep problems complaints and whose disease had been diagnosed by PSG and referred to the Sleep Disorders Research Center of Farabi Hospital, Kermanshah, between 2012 and 2015 were recruited in this study. To ft the best LR model, a model was frst ftted with all variables and then compared with a model made from the signifcant variables using Akaike’s information criterion (AIC). Te SVM model and radial basis function (RBF) kernel, whose parameters had been optimized by genetic algorithm, were used to diagnose OSA. Results: Based on AIC, the best LR model obtained from this study was a model ftted with all variables. Te performance of fnal LR model was compared with SVM model, revealing the accuracy 0.797 versus 0.729, sensitivity 0.714 versus 0.777, and specifcity 0.847 vs. 0.702, respectively. Conclusion: Both models were found to have an appropriate performance. However, considering accuracy as an important criterion for comparing the performance of models in this domain, it can be argued that SVM could have a better efciency than LR in diagnosing OSA in patients.