Computational and Mathematical Methods in Medicine
Volume 2012 (2012), Article ID 212498, 8 pages
Research Article

Prediagnosis of Obstructive Sleep Apnea via Multiclass MTS

1Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu 30013, Taiwan
2MBA Program in International Management, Department of Business Administration, Fu Jen Catholic University, New Taipei City 24205, Taiwan
3Department of Otolaryngology, Cathay General Hospital, Taipei 10630, Taiwan
4Quality Management Center, Cathay General Hospital, Taipei 10630, Taiwan
5Fu Jen Catholic University, School of medicine, New Taipei City 24205, Taiwan
6Information and Communications Research Laboratories, Industrial Technology Research Institute, Hsinchu 31040, Taiwan

Received 6 September 2011; Revised 9 January 2012; Accepted 16 January 2012

Academic Editor: Philip Crooke

Copyright © 2012 Chao-Ton Su et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.