ArticlePloS one2017
Prediction of the severity of obstructive sleep apnea by anthropometric features via support vector machine.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 3 of them syntheses that pooled it.
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Who cites it
24 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Performance of AI-Based Screening Tools for Obstructive Sleep Apnea Across Apnea-Hypopnea Index Thresholds: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Anthropometric measures and obstructive sleep apnea in children and adolescents: a systematic review of the literature and meta-analysis.Sleep & breathing = Schlaf & Atmung · 2024Pooled it
- Enabling Early Obstructive Sleep Apnea Diagnosis With Machine Learning: Systematic Review.Journal of medical Internet research · 2022Pooled it
- ACF-SAP: A machine learning framework for predicting obstructive sleep apnea severity using anthropometric and clinical features.Clinical neurophysiology practice · 2026Article
- Article
- Advancements in Obstructive Sleep Apnea Diagnosis and Screening Through Artificial Intelligence: A Systematic Review.Healthcare (Basel, Switzerland) · 2025Review
- Advances in Machine Learning Prediction Models for the Screening of Obstructive Sleep Apnea in Adults.Nature and science of sleep · 2025Review
- Exploring the complexity of obstructive sleep apnea: findings from machine learning on diagnosis and predictive capacity of individual factors.Sleep & breathing = Schlaf & Atmung · 2024Article
- Enhanced machine learning approaches for OSA patient screening: model development and validation study.Scientific reports · 2024Article
- Artificial intelligence-based predictive model for guidance on treatment strategy selection in oral and maxillofacial surgery.Heliyon · 2024Review
- OSApredictor: A tool for prediction of moderate to severe obstructive sleep apnea-hypopnea using readily available patient characteristics.Computers in biology and medicine · 2024Article
- Development and application of a machine learning-based predictive model for obstructive sleep apnea screening.Frontiers in big data · 2024Article
- Application and interpretation of machine learning models in predicting the risk of severe obstructive sleep apnea in adults.BMC medical informatics and decision making · 2023Article
- Predictive value of NoSAS questionnaire combined with the modified Mallampati grade for hypoxemia during routine sedation for gastrointestinal endoscopy.BMC anesthesiology · 2023Observational
- DOISNORE50: a perioperative sleep questionnaire predictive of obstructive sleep apnea and postoperative medical emergency team activation. A learning health system approach to sleep questionnaire development and screening.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2022Article
- Machine learning for image-based detection of patients with obstructive sleep apnea: an exploratory study.Sleep & breathing = Schlaf & Atmung · 2021Article
- Article
- Prediction Models for Obstructive Sleep Apnea in Korean Adults Using Machine Learning Techniques.Diagnostics (Basel, Switzerland) · 2021Article
- Screening of obstructive sleep apnea in patients who snore using a patch-type device with electrocardiogram and 3-axis accelerometer.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2020Article
- Support vector machine prediction of obstructive sleep apnea in a large-scale Chinese clinical sample.Sleep · 2020Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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