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

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• 作者：Kung-Jeng Wang ; Kun-Huang Chen ; Shou-Hung Huang…
• 关键词：Diagnosis model ; Fuzzy decision tree ; Obstructive sleep apnea
• 刊名：Journal of Medical Systems
• 出版年：2016
• 出版时间：April 2016
• 年：2016
• 卷：40
• 期：4
• 全文大小：912 KB
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• 作者单位：Kung-Jeng Wang (1)
  Kun-Huang Chen (1)
  Shou-Hung Huang (2) (3)
  Nai-Chia Teng (4) (5)
  
  1. Department of Industrial Management, National Taiwan University of Science and Technology, No.43, Sec. 4, Keelung Rd., Da’an Dist., Taipei, 106, Taiwan, Republic of China
  2. Department of Sleep Center, Taipei Medical University, Taipei, Taiwan, 110, Republic of China
  3. Department of Psychiatry & Psychiatric Research Center, Taipei Medical University, 110, Taipei, Taiwan, Republic of China
  4. School of Dentistry, College of Oral Medicine, Taipei Medical University, Taipei, 110, Taiwan, Republic of China
  5. Division of Oral Rehabilitation and Center of Pediatric Dentistry, Department of Dentistry, Taipei Medical University Hospital, Taipei, Taiwan, 110, Republic of China
  
• 刊物类别：Mathematics and Statistics
• 刊物主题：Statistics
  Statistics for Life Sciences, Medicine and Health Sciences
  Health Informatics and Administration
  
• 出版者：Springer Netherlands
• ISSN：1573-689X

文摘

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.