{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T18:08:57Z","timestamp":1775066937611,"version":"3.50.1"},"reference-count":25,"publisher":"Hindawi Limited","license":[{"start":{"date-parts":[[2012,1,1]],"date-time":"2012-01-01T00:00:00Z","timestamp":1325376000000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational and Mathematical Methods in Medicine"],"published-print":{"date-parts":[[2012]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.1155\/2012\/212498","type":"journal-article","created":{"date-parts":[[2012,4,5]],"date-time":"2012-04-05T11:55:46Z","timestamp":1333626946000},"page":"1-8","source":"Crossref","is-referenced-by-count":11,"title":["Prediagnosis of Obstructive Sleep Apnea via Multiclass MTS"],"prefix":"10.1155","volume":"2012","author":[{"given":"Chao-Ton","family":"Su","sequence":"first","affiliation":[{"name":"Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu 30013, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kun-Huang","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu 30013, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li-Fei","family":"Chen","sequence":"additional","affiliation":[{"name":"MBA Program in International Management, Department of Business Administration, Fu Jen Catholic University, New Taipei City 24205, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pa-Chun","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology, Cathay General Hospital, Taipei 10630, Taiwan"},{"name":"Quality Management Center, Cathay General Hospital, Taipei 10630, Taiwan"},{"name":"Fu Jen Catholic University, School of medicine, New Taipei City 24205, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu-Hsiang","family":"Hsiao","sequence":"additional","affiliation":[{"name":"Information and Communications Research Laboratories, Industrial Technology Research Institute, Hsinchu 31040, 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