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Application of Machine Learning in Rheumatic Immune Diseases

People are paying greater attention to their personal health as society develops and progresses, and rheumatic immunological disorders have become a serious concern that affects human health. As a result, research on a stable, trustworthy, and effective auxiliary diagnostic method for rheumatic immu...

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Detalles Bibliográficos
Autores principales: Li, Yuan, Zhao, Linru
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8808206/
https://www.ncbi.nlm.nih.gov/pubmed/35126955
http://dx.doi.org/10.1155/2022/9273641
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author Li, Yuan
Zhao, Linru
author_facet Li, Yuan
Zhao, Linru
author_sort Li, Yuan
collection PubMed
description People are paying greater attention to their personal health as society develops and progresses, and rheumatic immunological disorders have become a serious concern that affects human health. As a result, research on a stable, trustworthy, and effective auxiliary diagnostic method for rheumatic immune disorders is critical. Machine learning overcomes the inefficiencies and volatility of human data processing, ushering in a revolution in artificial intelligence research. With the use of big data, machine learning-based application research on rheumatic immunological disorders has already demonstrated detection abilities that are on par with or better than those of medical professionals. Artificial intelligence systems are now being applied in the field of rheumatic immune disorders, with an emphasis on the identification of patient joint images. This article focuses on the use of machine learning algorithms in the diagnosis of rheumatic illnesses, as well as the practical implications of disease-assisted diagnosis systems and intelligent medical diagnosis. This article focuses on three common machine learning algorithms for research and debate: logistic regression, support vector machines, and adaptive boosting techniques. The three algorithms are used to build diagnostic models based on rheumatic illness data, and the performance of each model is assessed. According to a thorough analysis of the assessment data, the diagnostic model based on the limit gradient boosting method has the best resilience. This article presents machine learning's use and advancement in rheumatic immunological disorders, as well as new ideas for investigating more appropriate and efficient diagnostic and treatment techniques.
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spelling pubmed-88082062022-02-03 Application of Machine Learning in Rheumatic Immune Diseases Li, Yuan Zhao, Linru J Healthc Eng Research Article People are paying greater attention to their personal health as society develops and progresses, and rheumatic immunological disorders have become a serious concern that affects human health. As a result, research on a stable, trustworthy, and effective auxiliary diagnostic method for rheumatic immune disorders is critical. Machine learning overcomes the inefficiencies and volatility of human data processing, ushering in a revolution in artificial intelligence research. With the use of big data, machine learning-based application research on rheumatic immunological disorders has already demonstrated detection abilities that are on par with or better than those of medical professionals. Artificial intelligence systems are now being applied in the field of rheumatic immune disorders, with an emphasis on the identification of patient joint images. This article focuses on the use of machine learning algorithms in the diagnosis of rheumatic illnesses, as well as the practical implications of disease-assisted diagnosis systems and intelligent medical diagnosis. This article focuses on three common machine learning algorithms for research and debate: logistic regression, support vector machines, and adaptive boosting techniques. The three algorithms are used to build diagnostic models based on rheumatic illness data, and the performance of each model is assessed. According to a thorough analysis of the assessment data, the diagnostic model based on the limit gradient boosting method has the best resilience. This article presents machine learning's use and advancement in rheumatic immunological disorders, as well as new ideas for investigating more appropriate and efficient diagnostic and treatment techniques. Hindawi 2022-01-25 /pmc/articles/PMC8808206/ /pubmed/35126955 http://dx.doi.org/10.1155/2022/9273641 Text en Copyright © 2022 Yuan Li and Linru Zhao. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Li, Yuan
Zhao, Linru
Application of Machine Learning in Rheumatic Immune Diseases
title Application of Machine Learning in Rheumatic Immune Diseases
title_full Application of Machine Learning in Rheumatic Immune Diseases
title_fullStr Application of Machine Learning in Rheumatic Immune Diseases
title_full_unstemmed Application of Machine Learning in Rheumatic Immune Diseases
title_short Application of Machine Learning in Rheumatic Immune Diseases
title_sort application of machine learning in rheumatic immune diseases
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8808206/
https://www.ncbi.nlm.nih.gov/pubmed/35126955
http://dx.doi.org/10.1155/2022/9273641
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