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

Research output: Contribution to journalArticlepeer-review

32 Citations (Scopus)


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. Results: 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. Conclusions: 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.

Original languageEnglish
Pages (from-to)317-323
Number of pages7
JournalSleep and Breathing
Issue number3
Publication statusPublished - Sept 2011


  • Genetic algorithm
  • Obstructive sleep apnea
  • Polysomnography

ASJC Scopus subject areas

  • Otorhinolaryngology
  • Clinical Neurology


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