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Application of various machine learning techniques to predict obstructive sleep apnea syndrome severity
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 supervise...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10115886/ https://www.ncbi.nlm.nih.gov/pubmed/37076549 http://dx.doi.org/10.1038/s41598-023-33170-7 |
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author | Han, Hyewon Oh, Junhyoung |
author_facet | Han, Hyewon Oh, Junhyoung |
author_sort | Han, Hyewon |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10115886 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-101158862023-04-21 Application of various machine learning techniques to predict obstructive sleep apnea syndrome severity Han, Hyewon Oh, Junhyoung Sci Rep Article 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. Nature Publishing Group UK 2023-04-19 /pmc/articles/PMC10115886/ /pubmed/37076549 http://dx.doi.org/10.1038/s41598-023-33170-7 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Han, Hyewon Oh, Junhyoung Application of various machine learning techniques to predict obstructive sleep apnea syndrome severity |
title | Application of various machine learning techniques to predict obstructive sleep apnea syndrome severity |
title_full | Application of various machine learning techniques to predict obstructive sleep apnea syndrome severity |
title_fullStr | Application of various machine learning techniques to predict obstructive sleep apnea syndrome severity |
title_full_unstemmed | Application of various machine learning techniques to predict obstructive sleep apnea syndrome severity |
title_short | Application of various machine learning techniques to predict obstructive sleep apnea syndrome severity |
title_sort | application of various machine learning techniques to predict obstructive sleep apnea syndrome severity |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10115886/ https://www.ncbi.nlm.nih.gov/pubmed/37076549 http://dx.doi.org/10.1038/s41598-023-33170-7 |
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