摘要
Obstructive sleep apnea (OSA) has become an important public health concern. Polysomnography (PSG) is traditionally considered an established and effective diagnostic tool providing information on the severity of OSA and the degree of sleep fragmentation. However, the numerous steps in the PSG test to diagnose OSA are costly and time consuming. This study aimed to apply the multiclass Mahalanobis-Taguchi system (MMTS) based on anthropometric information and questionnaire data to predict OSA. Implementation results showed that MMTS had an accuracy of 84.38% on the OSA prediction and achieved better performance compared to other approaches such as logistic regression, neural networks, support vector machine, C4.5 decision tree, and rough set. Therefore, MMTS can assist doctors in prediagnosis of OSA before running the PSG test, thereby enabling the more effective use of medical resources.
原文 | 英語 |
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文章編號 | 212498 |
期刊 | Computational and Mathematical Methods in Medicine |
卷 | 2012 |
DOIs | |
出版狀態 | 已發佈 - 2012 |
對外發佈 | 是 |
ASJC Scopus subject areas
- 建模與模擬
- 一般生物化學,遺傳學和分子生物學
- 一般免疫學和微生物學
- 應用數學