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Aortic Dissection Auxiliary Diagnosis Model and Applied Research Based on Ensemble Learning

Aortic dissection (AD), a dangerous disease threatening to human beings, has a hidden onset and rapid progression and has few effective methods in its early diagnosis. At present, although CT angiography acts as the gold standard on AD diagnosis, it is so expensive and time-consuming that it can har...

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Autores principales: Luo, Jingmin, Zhang, Wei, Tan, Shiyang, Liu, Lijue, Bai, Yongping, Zhang, Guogang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8733407/
https://www.ncbi.nlm.nih.gov/pubmed/35004892
http://dx.doi.org/10.3389/fcvm.2021.777757
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author Luo, Jingmin
Zhang, Wei
Tan, Shiyang
Liu, Lijue
Bai, Yongping
Zhang, Guogang
author_facet Luo, Jingmin
Zhang, Wei
Tan, Shiyang
Liu, Lijue
Bai, Yongping
Zhang, Guogang
author_sort Luo, Jingmin
collection PubMed
description Aortic dissection (AD), a dangerous disease threatening to human beings, has a hidden onset and rapid progression and has few effective methods in its early diagnosis. At present, although CT angiography acts as the gold standard on AD diagnosis, it is so expensive and time-consuming that it can hardly offer practical help to patients. Meanwhile, the artificial intelligence technology may provide a cheap but effective approach to building an auxiliary diagnosis model for improving the early AD diagnosis rate by taking advantage of the data of the general conditions of AD patients, such as the data about the basic inspection information. Therefore, this study proposes to hybrid five types of machine learning operators into an integrated diagnosis model, as an auxiliary diagnostic approach, to cooperate with the AD-clinical analysis. To improve the diagnose accuracy, the participating rate of each operator in the proposed model may adjust adaptively according to the result of the data learning. After a set of experimental evaluations, the proposed model, acting as the preliminary AD-discriminant, has reached an accuracy of over 80%, which provides a promising instance for medical colleagues.
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spelling pubmed-87334072022-01-07 Aortic Dissection Auxiliary Diagnosis Model and Applied Research Based on Ensemble Learning Luo, Jingmin Zhang, Wei Tan, Shiyang Liu, Lijue Bai, Yongping Zhang, Guogang Front Cardiovasc Med Cardiovascular Medicine Aortic dissection (AD), a dangerous disease threatening to human beings, has a hidden onset and rapid progression and has few effective methods in its early diagnosis. At present, although CT angiography acts as the gold standard on AD diagnosis, it is so expensive and time-consuming that it can hardly offer practical help to patients. Meanwhile, the artificial intelligence technology may provide a cheap but effective approach to building an auxiliary diagnosis model for improving the early AD diagnosis rate by taking advantage of the data of the general conditions of AD patients, such as the data about the basic inspection information. Therefore, this study proposes to hybrid five types of machine learning operators into an integrated diagnosis model, as an auxiliary diagnostic approach, to cooperate with the AD-clinical analysis. To improve the diagnose accuracy, the participating rate of each operator in the proposed model may adjust adaptively according to the result of the data learning. After a set of experimental evaluations, the proposed model, acting as the preliminary AD-discriminant, has reached an accuracy of over 80%, which provides a promising instance for medical colleagues. Frontiers Media S.A. 2021-12-23 /pmc/articles/PMC8733407/ /pubmed/35004892 http://dx.doi.org/10.3389/fcvm.2021.777757 Text en Copyright © 2021 Luo, Zhang, Tan, Liu, Bai and Zhang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Cardiovascular Medicine
Luo, Jingmin
Zhang, Wei
Tan, Shiyang
Liu, Lijue
Bai, Yongping
Zhang, Guogang
Aortic Dissection Auxiliary Diagnosis Model and Applied Research Based on Ensemble Learning
title Aortic Dissection Auxiliary Diagnosis Model and Applied Research Based on Ensemble Learning
title_full Aortic Dissection Auxiliary Diagnosis Model and Applied Research Based on Ensemble Learning
title_fullStr Aortic Dissection Auxiliary Diagnosis Model and Applied Research Based on Ensemble Learning
title_full_unstemmed Aortic Dissection Auxiliary Diagnosis Model and Applied Research Based on Ensemble Learning
title_short Aortic Dissection Auxiliary Diagnosis Model and Applied Research Based on Ensemble Learning
title_sort aortic dissection auxiliary diagnosis model and applied research based on ensemble learning
topic Cardiovascular Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8733407/
https://www.ncbi.nlm.nih.gov/pubmed/35004892
http://dx.doi.org/10.3389/fcvm.2021.777757
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