THE IMPROVEMENT OF PCA ALGORITHM AND ITS APPLICATION IN THE PREDICTION OF ELBOW KNEE JOINT INJURY
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Revista brasileira de medicina do esporte (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1517-86922021000500518 |
Resumo: | ABSTRACT Objective: There were many constraints produced by training time and joint injury to analyze the influence of the training intensity on the elbow and knee joints of athletes during the training process. Methods: An improved algorithm-based master component analysis (PCA) modeling method is proposed .1 4 4 athletes were selected in xxx and compared in three groups. Results: The improved PCA models for injury prediction were applied to athletes from group A, the traditional injury models for prediction were adopted for athletes from group B, and athletes from group C received the hospital physical examinations. The results showed that the accuracy of elbow injury in group A due to excessive exercise was 66.86%, the accuracy of hospital physical examination in group C was 67%, and the accuracy of the traditional algorithm in group B was 50%, finding that the accuracy of group A was obviously different from group B (P < 0.05). Compared with other injuries caused by excessive friction, the detection accuracy of knee injuries caused by excessive friction in group A was 62%, that in group B was 44%, and that in group C was 63%. There was a statistically marked difference between groups A and B (P < 0.05). Conclusions: A PCA - based model of athletes’ overtraining injury has high accuracy and adaptability, predicting elbow injury. Level of evidence II; Therapeutic studies - investigation of treatment results. |
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THE IMPROVEMENT OF PCA ALGORITHM AND ITS APPLICATION IN THE PREDICTION OF ELBOW KNEE JOINT INJURYElbow jointWounds and injuriesAthleteABSTRACT Objective: There were many constraints produced by training time and joint injury to analyze the influence of the training intensity on the elbow and knee joints of athletes during the training process. Methods: An improved algorithm-based master component analysis (PCA) modeling method is proposed .1 4 4 athletes were selected in xxx and compared in three groups. Results: The improved PCA models for injury prediction were applied to athletes from group A, the traditional injury models for prediction were adopted for athletes from group B, and athletes from group C received the hospital physical examinations. The results showed that the accuracy of elbow injury in group A due to excessive exercise was 66.86%, the accuracy of hospital physical examination in group C was 67%, and the accuracy of the traditional algorithm in group B was 50%, finding that the accuracy of group A was obviously different from group B (P < 0.05). Compared with other injuries caused by excessive friction, the detection accuracy of knee injuries caused by excessive friction in group A was 62%, that in group B was 44%, and that in group C was 63%. There was a statistically marked difference between groups A and B (P < 0.05). Conclusions: A PCA - based model of athletes’ overtraining injury has high accuracy and adaptability, predicting elbow injury. Level of evidence II; Therapeutic studies - investigation of treatment results.Sociedade Brasileira de Medicina do Exercício e do Esporte2021-09-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1517-86922021000500518Revista Brasileira de Medicina do Esporte v.27 n.5 2021reponame:Revista brasileira de medicina do esporte (Online)instname:Sociedade Brasileira de Medicina do Exercício e do Esporte (SBMEE)instacron:SBMEE10.1590/1517-8692202127042021_0120info:eu-repo/semantics/openAccessLiu,Zhenhuaeng2021-11-24T00:00:00Zoai:scielo:S1517-86922021000500518Revistahttp://www.scielo.br/rbmeONGhttps://old.scielo.br/oai/scielo-oai.php||revista@medicinadoesporte.org.br1806-99401517-8692opendoar:2021-11-24T00:00Revista brasileira de medicina do esporte (Online) - Sociedade Brasileira de Medicina do Exercício e do Esporte (SBMEE)false |
dc.title.none.fl_str_mv |
THE IMPROVEMENT OF PCA ALGORITHM AND ITS APPLICATION IN THE PREDICTION OF ELBOW KNEE JOINT INJURY |
title |
THE IMPROVEMENT OF PCA ALGORITHM AND ITS APPLICATION IN THE PREDICTION OF ELBOW KNEE JOINT INJURY |
spellingShingle |
THE IMPROVEMENT OF PCA ALGORITHM AND ITS APPLICATION IN THE PREDICTION OF ELBOW KNEE JOINT INJURY Liu,Zhenhua Elbow joint Wounds and injuries Athlete |
title_short |
THE IMPROVEMENT OF PCA ALGORITHM AND ITS APPLICATION IN THE PREDICTION OF ELBOW KNEE JOINT INJURY |
title_full |
THE IMPROVEMENT OF PCA ALGORITHM AND ITS APPLICATION IN THE PREDICTION OF ELBOW KNEE JOINT INJURY |
title_fullStr |
THE IMPROVEMENT OF PCA ALGORITHM AND ITS APPLICATION IN THE PREDICTION OF ELBOW KNEE JOINT INJURY |
title_full_unstemmed |
THE IMPROVEMENT OF PCA ALGORITHM AND ITS APPLICATION IN THE PREDICTION OF ELBOW KNEE JOINT INJURY |
title_sort |
THE IMPROVEMENT OF PCA ALGORITHM AND ITS APPLICATION IN THE PREDICTION OF ELBOW KNEE JOINT INJURY |
author |
Liu,Zhenhua |
author_facet |
Liu,Zhenhua |
author_role |
author |
dc.contributor.author.fl_str_mv |
Liu,Zhenhua |
dc.subject.por.fl_str_mv |
Elbow joint Wounds and injuries Athlete |
topic |
Elbow joint Wounds and injuries Athlete |
description |
ABSTRACT Objective: There were many constraints produced by training time and joint injury to analyze the influence of the training intensity on the elbow and knee joints of athletes during the training process. Methods: An improved algorithm-based master component analysis (PCA) modeling method is proposed .1 4 4 athletes were selected in xxx and compared in three groups. Results: The improved PCA models for injury prediction were applied to athletes from group A, the traditional injury models for prediction were adopted for athletes from group B, and athletes from group C received the hospital physical examinations. The results showed that the accuracy of elbow injury in group A due to excessive exercise was 66.86%, the accuracy of hospital physical examination in group C was 67%, and the accuracy of the traditional algorithm in group B was 50%, finding that the accuracy of group A was obviously different from group B (P < 0.05). Compared with other injuries caused by excessive friction, the detection accuracy of knee injuries caused by excessive friction in group A was 62%, that in group B was 44%, and that in group C was 63%. There was a statistically marked difference between groups A and B (P < 0.05). Conclusions: A PCA - based model of athletes’ overtraining injury has high accuracy and adaptability, predicting elbow injury. Level of evidence II; Therapeutic studies - investigation of treatment results. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-09-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1517-86922021000500518 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1517-86922021000500518 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/1517-8692202127042021_0120 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Sociedade Brasileira de Medicina do Exercício e do Esporte |
publisher.none.fl_str_mv |
Sociedade Brasileira de Medicina do Exercício e do Esporte |
dc.source.none.fl_str_mv |
Revista Brasileira de Medicina do Esporte v.27 n.5 2021 reponame:Revista brasileira de medicina do esporte (Online) instname:Sociedade Brasileira de Medicina do Exercício e do Esporte (SBMEE) instacron:SBMEE |
instname_str |
Sociedade Brasileira de Medicina do Exercício e do Esporte (SBMEE) |
instacron_str |
SBMEE |
institution |
SBMEE |
reponame_str |
Revista brasileira de medicina do esporte (Online) |
collection |
Revista brasileira de medicina do esporte (Online) |
repository.name.fl_str_mv |
Revista brasileira de medicina do esporte (Online) - Sociedade Brasileira de Medicina do Exercício e do Esporte (SBMEE) |
repository.mail.fl_str_mv |
||revista@medicinadoesporte.org.br |
_version_ |
1752122237717053440 |