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Sleep and Breathing

, Volume 20, Issue 2, pp 509–514 | Cite as

A prediction model based on artificial neural networks for the diagnosis of obstructive sleep apnea

  • Harun KaramanliEmail author
  • Tankut Yalcinoz
  • Mehmet Akif Yalcinoz
  • Tuba Yalcinoz
Original Article

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.

Method

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.

Keywords

Obstructive sleep apnea Artificial neural networks Polysomnography Diagnostic accuracy 

Notes

Financial/nonfinancial disclosures

Drs Karamanlı, Yalcinoz have reported that no potential conflicts of interest exist with any companies/organizations whose products or services may be discussed in this article.

Role of sponsors

The sponsors had no role in the design of the study, the collection and analysis of the data, or the preparation of the manuscript.

Conflict of interest

The authors declare that they have no conflict of interests regarding the publication of this paper.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Harun Karamanli
    • 1
    Email author
  • Tankut Yalcinoz
    • 2
  • Mehmet Akif Yalcinoz
    • 3
  • Tuba Yalcinoz
    • 3
  1. 1.Department of Pulmonology, Faculty of MedicineMevlana UniversityMeram-KonyaTurkey
  2. 2.Department of Electrical and Electronics EngineeringMevlana UniversityKonyaTurkey
  3. 3.Barış CadKonyaTurkey

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