Prediagnosis of Obstructive Sleep Apnea via Multiclass MTS

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...

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Bibliographic Details
Published in:Computational and mathematical methods in medicine 2012-01, Vol.2012 (2012), p.1-8
Main Authors: Wang, Pa-Chun, Chen, Kun-Huang, Chen, Li-Fei, Hsiao, Yu-Hsiang, Su, Chao-Ton
Format: Article
Language:English
Subjects:
Adolescent
Adult
Aged
Child
Female
Humans
Male
Middle Aged
Polysomnography - methods
Sensitivity and Specificity
Sleep Apnea, Obstructive - diagnosis
Surveys and Questionnaires
Young Adult
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Summary: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.
ISSN:1748-670X
1748-6718
DOI:10.1155/2012/212498