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

Wen Te Liu, Hau Tieng Wu, Jer Nan Juang, Adam Wisniewski, Hsin Chien Lee, Dean Wu, Yu Lun Lo

研究成果: 雜誌貢獻文章

2 引文 (Scopus)

摘要

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.
原文英語
文章編號e0176991
期刊PLoS One
12
發行號5
DOIs
出版狀態已發佈 - 五月 1 2017

指紋

sleep apnea
Obstructive Sleep Apnea
Support vector machines
prediction
Databases
Waist Circumference
Apnea
apnea
gender
artificial intelligence
waist circumference
Testing
Body Mass Index
Neck
neck
body mass index
Learning systems
Screening
Aging of materials
testing

ASJC Scopus subject areas

  • Medicine(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)

引用此文

Prediction of the severity of obstructive sleep apnea by anthropometric features via support vector machine. / Liu, Wen Te; Wu, Hau Tieng; Juang, Jer Nan; Wisniewski, Adam; Lee, Hsin Chien; Wu, Dean; Lo, Yu Lun.

於: PLoS One, 卷 12, 編號 5, e0176991, 01.05.2017.

研究成果: 雜誌貢獻文章

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