A Prognosis Tool Based on Fuzzy Anthropometric and Questionnaire Data for Obstructive Sleep Apnea Severity

  • Kung-Jeng Wang
  • Kun-Huang Chen
  • Shou-Hung Huang
  • Nai-Chia Teng
Systems-Level Quality Improvement
Part of the following topical collections:
  1. Systems-Level Quality Improvement


Obstructive sleep apnea (OSA) are linked to the augmented risk of morbidity and mortality. Although polysomnography is considered a well-established method for diagnosing OSA, it suffers the weakness of time consuming and labor intensive, and requires doctors and attending personnel to conduct an overnight evaluation in sleep laboratories with dedicated systems. This study aims at proposing an efficient diagnosis approach for OSA on the basis of anthropometric and questionnaire data. The proposed approach integrates fuzzy set theory and decision tree to predict OSA patterns. A total of 3343 subjects who were referred for clinical suspicion of OSA (eventually 2869 confirmed with OSA and 474 otherwise) were collected, and then classified by the degree of severity. According to an assessment of experiment results on g-means, our proposed method outperforms other methods such as linear regression, decision tree, back propagation neural network, support vector machine, and learning vector quantization. The proposed method is highly viable and capable of detecting the severity of OSA. It can assist doctors in pre-diagnosis of OSA before running the formal PSG test, thereby enabling the more effective use of medical resources.


Diagnosis model Fuzzy decision tree Obstructive sleep apnea 



This work is partially supported by the National Science Council, R.O.C. (Taiwan), and National Taiwan University of Science and Technology - Taipei Medical University Joint Research Program (TMU-NTUST-102-06 & TMU-NTUST-101-07). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Compliance with Ethical Standards

Conflict of Interest

This study has no conflict of interest to any parties/agencies.


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Kung-Jeng Wang
    • 1
  • Kun-Huang Chen
    • 1
  • Shou-Hung Huang
    • 2
    • 3
  • Nai-Chia Teng
    • 4
    • 5
  1. 1.Department of Industrial ManagementNational Taiwan University of Science and TechnologyTaipeiRepublic of China
  2. 2.Department of Sleep CenterTaipei Medical UniversityTaipeiRepublic of China
  3. 3.Department of Psychiatry & Psychiatric Research CenterTaipei Medical UniversityTaipeiRepublic of China
  4. 4.School of Dentistry, College of Oral MedicineTaipei Medical UniversityTaipeiRepublic of China
  5. 5.Division of Oral Rehabilitation and Center of Pediatric Dentistry, Department of DentistryTaipei Medical University HospitalTaipeiRepublic of China

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