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

研究成果: 雜誌貢獻文章

3 引文 斯高帕斯(Scopus)

摘要

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.
原文英語
文章編號110
期刊Journal of Medical Systems
40
發行號4
DOIs
出版狀態已發佈 - 四月 1 2016

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Health Informatics
  • Health Information Management
  • Information Systems

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