Prediction of the severity of obstructive sleep apnea by anthropometric features via support vector machine

• Published in: PloS one Vol. 12; no. 5; p. e0176991
• Main Authors: Liu, Wen-Te, Wu, Hau-tieng, Juang, Jer-Nan, Wisniewski, Adam, Lee, Hsin-Chien, Wu, Dean, Lo, Yu-Lun
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 04.05.2017; Public Library of Science (PLoS)
• Subjects: Adipose tissue; Adult; Age; Anthropometry; Apnea; Biology and Life Sciences; Body fat; Body mass; Body mass index; Body measurements; Body size; Computer and Information Sciences; Disasters; Engineering; Fatigue; Female; Females; Gas exchange; Gender; Gender differences; Globalization; Health aspects; Health care; Hormone replacement therapy; Hospitals; Humans; Insomnia; Internal medicine; Laboratories; Learning algorithms; Likelihood ratio; Machine learning; Male; Medicine; Medicine and Health Sciences; Menopause; Middle Aged; Minority & ethnic groups; Models, Theoretical; Neck; Patients; Physical Sciences; Prediction models; Predictions; Public health; Questionnaires; Research and Analysis Methods; Resonance; Science; Severity of Illness Index; Sleep; Sleep apnea; Sleep Apnea, Obstructive - physiopathology; Sleep disorders; Socioeconomics; Specifications; Studies; Subgroups; Support Vector Machine; Support vector machines; Teaching models; Ventilation; Womens health; Taiwan

To develop an applicable prediction for obstructive sleep apnea (OSA) is still a challenge in clinical practice. We apply a modern machine learning method, the support vector machine to establish a predicting model for the severity of OSA. The support vector machine was applied to build up a predict...