title: A prediction model based on artificial neural networks for the diagnosis of obstructive sleep apnea.
abstract: BACKGROUND
  Recently, artificial neural networks (ANNs) have been widely applied in science, engineering, and medicine. In the present study, we evaluated the ability of artificial neural networks to be used as a computer program and assistant tool in the diagnosis of obstructive sleep apnea (OSA). Our hypothesis was that ANNs could use clinical information to precisely predict cases of OSA.
  
  
  METHODS
  The study population in this clinical trial consisted of 201 patients with suspected OSA (140 with a positive diagnosis of OSA and 61 with a negative diagnosis of OSA). The artificial neural network was trained by assessing five clinical variables from 201 patients; efficiency was then estimated in this group of 201 patients. The patients were classified using a five-element input vector. ANN classifiers were assessed with the multilayer perceptron (MLP) networks.
  
  
  RESULTS
  Use of the MLP classifiers resulted in a diagnostic accuracy of 86.6 %, which in clinical practice is high enough to reduce the number of patients evaluated by polysomnography (PSG), an expensive and limited diagnostic resource.
  
  
  CONCLUSIONS
  By establishing a pattern that allows the recognition of OSA, ANNs can be used to identify patients requiring PSG.
authors.affiliation: Department of Pulmonology, Faculty of Medicine, Mevlana University, Aksinne Neighborhood Esmetas Street No: 16, 42040, Meram-Konya, Turkey. drharun@hotmail.com.
authors.forename: Harun
authors.initials: H
authors.lastname: Karamanli
authors.affiliation: Department of Electrical and Electronics Engineering, Mevlana University, Konya, Turkey.
authors.forename: Tankut
authors.initials: T
authors.lastname: Yalcinoz
authors.affiliation: Barış Cad, Konya, 42003, Turkey.
authors.forename: Mehmet Akif
authors.initials: MA
authors.lastname: Yalcinoz
authors.affiliation: Barış Cad, Konya, 42003, Turkey.
authors.forename: Tuba
authors.initials: T
authors.lastname: Yalcinoz
mesh_terms: D017046:Cost Savings; D003936:Diagnosis, Computer-Assisted / Q000191:economics; D006801:Humans; D016571:Neural Networks, Computer; D017286:Polysomnography / Q000191:economics; D011237:Predictive Value of Tests; D020181:Sleep Apnea, Obstructive / Q000175:diagnosis* / Q000191:economics
keywords: Artificial neural networks; Diagnostic accuracy; Obstructive sleep apnea; Polysomnography