ArticleScientific reports2023
Application of various machine learning techniques to predict obstructive sleep apnea syndrome severity.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline 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
- Development of a machine learning model using systemic and ophthalmic parameters to detect sleep-disordered breathing in glaucoma patients.Japanese journal of ophthalmology · 2026Article
- Development and Explainable Machine Learning Validation of a Novel Sleep Disturbance Ratio for Obstructive Sleep Apnea Severity Assessment.Diagnostics (Basel, Switzerland) · 2026Article
- Three-class obstructive sleep apnea severity assessment: a parallel AHI and ODI explainable artificial intelligence framework using craniofacial-enriched clinical data.Sleep & breathing = Schlaf & Atmung · 2026Article
- Development and validation of machine learning models for obstructive sleep apnea risk stratification in high-risk pregnant women.BMC pregnancy and childbirth · 2026Observational
- Machine Learning-Based Multidimensional Oximetry for Obstructive Sleep Apnea Screening: Development and External Validation.JMIR medical informatics · 2026Article
- Advancements, challenges, and prospects of explainable AI in sleep disordered breathing.Sleep & breathing = Schlaf & Atmung · 2026Review
- Bridging Data, Semantics, and Clinical Reasoning: A Knowledge Graph Framework for Pediatric Obstructive Sleep Apnea.Children (Basel, Switzerland) · 2026Article
- "Phenotyping obstructive sleep apnea: a pathway to precision medicine".Sleep & breathing = Schlaf & Atmung · 2026Review
- Machine learning optimization of obstructive sleep apnea screening: development and validation of a gradient boosting prediction model with a clinical implementation framework.Frontiers in medicine · 2026Article
- Machine learning approach for predicting the severity risk of obstructive sleep apnea syndrome.Frontiers in big data · 2026Article
- Advances in Machine Learning Prediction Models for the Screening of Obstructive Sleep Apnea in Adults.Nature and science of sleep · 2025Review
- From Big Data to AI-Driven Decisions in Obstructive Sleep Apnea: A Narrative Review Integrating the DDPP Framework.Nature and science of sleep · 2025Review
- Analysis of upper airway CT-based radiomics in adult obstructive sleep apnea.Frontiers in medicine · 2025Article
- Automatic prediction of obstructive sleep apnea in patients with temporomandibular disorder based on multidata and machine learning.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
- Application of LightGBM hybrid model based on TPE algorithm optimization in sleep apnea detection.Frontiers in neuroscience · 2024Article
- Machine learning predictive models for grading bronchopulmonary dysplasia: umbilical cord blood IL-6 as a biomarker.Frontiers in pediatrics · 2023Article
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2 authors.
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Abstract
As the incidence of obstructive sleep apnea syndrome (OSAS) increases worldwide, the need for a new screening method that can compensate for the shortcomings of the traditional diagnostic method, polysomnography (PSG), is emerging. In this study, data from 4014 patients were used, and both supervised and unsupervised learning methods were used. Clustering was conducted with hierarchical agglomerative clustering, K-means, bisecting K-means algorithm, Gaussian mixture model, and feature engineering was carried out using both medically researched methods and machine learning techniques. For classification, we used gradient boost-based models such as XGBoost, LightGBM, CatBoost, and Random Forest to predict the severity of OSAS. The developed model showed high performance with 88%, 88%, and 91% of classification accuracy for three thresholds for the severity of OSAS: Apnea-Hypopnea Index (AHI) [Formula: see text] 5, AHI [Formula: see text] 15, and AHI [Formula: see text] 30, respectively. The results of this study demonstrate significant evidence of sufficient potential to utilize machine learning in predicting OSAS severity.
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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 Socratic graph.