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
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0176991

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 prediction model based on three anthropometric features (neck circumference, waist circumference, and body mass index) and age on the first database. The established model was then valided independently on the second database. The anthropometric features and age were combined to generate powerful predictors for OSA. Following the common practice, we predict if a subject has the apnea-hypopnea index greater then 15 or not as well as 30 or not. Dividing by genders and age, for the AHI threhosld 15 (respectively 30), the cross validation and testing accuracy for the prediction were 85.3% and 76.7% (respectively 83.7% and 75.5%) in young female, while the negative likelihood ratio for the AHI threhosld 15 (respectively 30) for the cross validation and testing were 0.2 and 0.32 (respectively 0.06 and 0.1) in young female. The more accurate results with lower negative likelihood ratio in the younger patients, especially the female subgroup, reflect the potential of the proposed model for the screening purpose and the importance of approaching by different genders and the effects of aging.