ArticleSleep2020
Support vector machine prediction of obstructive sleep apnea in a large-scale Chinese clinical sample.
Article in Sleep, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it, 71 citations in OpenAlex.
- 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
- 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
- Prospective Validation and Usability Evaluation of a Mobile Diagnostic App for Obstructive Sleep Apnea.Diagnostics (Basel, Switzerland) · 2024Article
- Inclusion of blood pressure parameter increases predictive capability of severe obstructive sleep apnea: A decision tree approach.Journal of clinical hypertension (Greenwich, Conn.) · 2024Article
- Enhanced machine learning approaches for OSA patient screening: model development and validation study.Scientific reports · 2024Article
- OSApredictor: A tool for prediction of moderate to severe obstructive sleep apnea-hypopnea using readily available patient characteristics.Computers in biology and medicine · 2024Article
- Application of Machine Learning Techniques for the Diagnosis of Obstructive Sleep Apnea/Hypopnea Syndrome.Life (Basel, Switzerland) · 2024Article
- Paediatric sleep diagnostics in the 21st century: the era of "sleep-omics"?European respiratory review : an official journal of the European Respiratory Society · 2024Review
- Design and Conceptual Proposal of an Intelligent Clinical Decision Support System for the Diagnosis of Suspicious Obstructive Sleep Apnea Patients from Health Profile.International journal of environmental research and public health · 2023Article
- Sleep disorder and apnea events detection framework with high performance using two-tier learning model design.PeerJ. Computer science · 2023Article
- A Novel Clinical Tool to Detect Severe Obstructive Sleep Apnea.Nature and science of sleep · 2023Article
- Review of Application of Machine Learning as a Screening Tool for Diagnosis of Obstructive Sleep Apnea.Medicina (Kaunas, Lithuania) · 2022Review
- Lower 24-h urinary potassium excretion is negatively associated with excessive daytime sleepiness in the general population.Sleep & breathing = Schlaf & Atmung · 2022Article
- Predicting acupuncture efficacy for functional dyspepsia based on routine clinical features: a machine learning study in the framework of predictive, preventive, and personalized medicine.The EPMA journal · 2022Article
- Modulation of Sleep Architecture by Whole-Body Static Magnetic Exposure: A Study Based on EEG-Based Automatic Sleep Staging.International journal of environmental research and public health · 2022Article
- Classification of severe obstructive sleep apnea with cognitive impairment using degree centrality: A machine learning analysis.Frontiers in neurology · 2022Article
- A Novel Quantitative Arousal-Associated EEG-Metric to Predict Severity of Respiratory Distress in Obstructive Sleep Apnea Patients.Frontiers in physiology · 2022Article
- Towards Validating the Effectiveness of Obstructive Sleep Apnea Classification from Electronic Health Records Using Machine Learning.Healthcare (Basel, Switzerland) · 2021Article
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Corrections and comments
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
STUDY
objectivesPolysomnography is the gold standard for diagnosis of obstructive sleep apnea (OSA) but it is costly and access is often limited. The aim of this study is to develop a clinically useful support vector machine (SVM)-based prediction model to identify patients with high probability of OSA for nonsleep specialist physician in clinical practice.
methodsThe SVM model was developed using the features routinely collected at the clinical evaluation from 6,875 Chinese patients referred to sleep clinics for suspected OSA. Three apnea-hypopnea index (AHI) cutoffs, ≥5/h, ≥15/h, and ≥30/h were used to define the severity of OSA. The continuous and categorized features were selected separately and were further selected through stepwise forward feature selection. The modeling was achieved through fivefold cross-validation. The model discriminative ability was evaluated for the whole data set and four subgroups categorized with gender and age (<65 versus ≥65 years old [y/o]).
resultsTwo features were selected to predict AHI cutoff ≥5/h with six features selected for ≥15/h, and six features selected for ≥30/h, respectively, to reach Area under the Receiver Operating Characteristic (AUROC) 0.82, 0.80, and 0.78, respectively. The sensitivity was 74.14%, 75.18%, and 70.26%, while the specificity was 74.71%, 68.73%, and 70.30%, respectively. Compared to logistic regression, Berlin questionnaire, NoSAS Score, and Supersparse Linear Integer Model (SLIM) scoring system, the SVM model performs better with a more balanced sensitivity and specificity. The discriminative ability was best for male <65 y/o and modest for female ≥65 y/o.
conclusionOur model provides a simple and accurate modality for early identification of patients with OSA and may potentially help prioritize them for sleep study.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.