Sleep and Breathing

, Volume 15, Issue 3, pp 317–323 | Cite as

A prediction model based on an artificial intelligence system for moderate to severe obstructive sleep apnea

  • Lei Ming Sun
  • Hung-Wen Chiu
  • Chih Yuan Chuang
  • Li Liu
Original Article


Study objectives

Obstructive sleep apnea (OSA) is a major concern in modern medicine; however, it is difficult to diagnose. Screening questionnaires such as the Berlin questionnaire, Rome questionnaire, and BASH'IM score are used to identify patients with OSA. However, the sensitivity and specificity of these tools are not satisfactory. We aim to introduce an artificial intelligence method to screen moderate to severe OSA patients (apnea–hypopnea index ≧15).

Patients and methods

One hundred twenty patients were asked to complete a newly developed questionnaire before undergoing an overnight polysomnography (PSG) study. One hundred ten validated questionnaires were enrolled in this study. Genetic algorithm (GA) was used to build the five best models based on these questionnaires. The same data were analyzed with logistic regression (LR) for comparison.


The sensitivity of the GA models varied from 81.8% to 88.0%, with a specificity of 95% to 97%. On the other hand, the sensitivity and specificity of the LR model were 55.6% and 57.9%, respectively.


GA provides a good solution to build models for screening moderate to severe OSA patients, who require PSG evaluation and medical intervention. The questionnaire did not require any special biochemistry data and was easily self-administered. The sensitivity and specificity of the GA models are satisfactory and may improve when more patients are recruited.


Obstructive sleep apnea Genetic algorithm Polysomnography 


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Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Lei Ming Sun
    • 1
  • Hung-Wen Chiu
    • 1
  • Chih Yuan Chuang
    • 2
  • Li Liu
    • 1
  1. 1.Graduate Institute of Medical InformaticsTaipei Medical UniversityTaipeiTaiwan
  2. 2.Sleep Center of Far Eastern Medical HospitalTaipeiTaiwan

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