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A Factor Analysis Model for Rapid Evaluation of the Semen Quality of Fertile Men in China

OBJECTIVE: The objective of this study is to reduce the dimension of several indicators with a strong correlation when conducting semen quality analysis in a small number of comprehensive variables that could retain most of the information in the original variables. METHODS: A total of 1132 subjects...

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Autores principales: Wang, Ning, Song, Meifang, Gu, Haike, Gao, Yiyuan, Yu, Ge, Lv, Fang, Shi, Cuige, Wang, Shangming, Sun, Liwen, Xiao, Yang, Zhang, Shucheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8901226/
https://www.ncbi.nlm.nih.gov/pubmed/35264856
http://dx.doi.org/10.2147/JMDH.S341444
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author Wang, Ning
Song, Meifang
Gu, Haike
Gao, Yiyuan
Yu, Ge
Lv, Fang
Shi, Cuige
Wang, Shangming
Sun, Liwen
Xiao, Yang
Zhang, Shucheng
author_facet Wang, Ning
Song, Meifang
Gu, Haike
Gao, Yiyuan
Yu, Ge
Lv, Fang
Shi, Cuige
Wang, Shangming
Sun, Liwen
Xiao, Yang
Zhang, Shucheng
author_sort Wang, Ning
collection PubMed
description OBJECTIVE: The objective of this study is to reduce the dimension of several indicators with a strong correlation when conducting semen quality analysis in a small number of comprehensive variables that could retain most of the information in the original variables. METHODS: A total of 1132 subjects were recruited from the Maternal and Child Health Institutions of seven provinces in mainland China. They completed the questionnaire and provided semen samples. Visualization of the correlation between variables was realized by using a function chart and correlation in the PerformanceAnalytics package of the R programming language (version 3.6.3 [2020-02-29]). Factor analysis was conducted using the principal function in the psych package of R. Principal component analysis, combined with varimax rotation, was used in the operation of the model, and two common factors were selected and measured to provide values for the common factor. The score coefficient was estimated using the regression method. RESULTS: The contribution rates of the two common factors to variable X were 43.7% and 33.98%, respectively. When the two common factors were selected, approximately 78% of the information of the original variables could be explained. The correlation coefficients between the first common factor (the quantitative factor) and sperm density, total sperm count, and semen volume were 0.824, 0.984, and 0.544, respectively. The correlation coefficients between the second common factor (the quality factor) and sperm motility and the percentage of forward-moving (progressive spermatozoa) sperm were 0.978 and 0.976, respectively. CONCLUSION: The correlation between the original variables of a semen quality analysis was strong and suitable for dimensionality reduction by factor analysis. Factor analysis and dimensionality reduction provide a fast and accurate assessment of semen quality. Patients with low fertility or infertility can be identified and provided with corresponding treatments.
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spelling pubmed-89012262022-03-08 A Factor Analysis Model for Rapid Evaluation of the Semen Quality of Fertile Men in China Wang, Ning Song, Meifang Gu, Haike Gao, Yiyuan Yu, Ge Lv, Fang Shi, Cuige Wang, Shangming Sun, Liwen Xiao, Yang Zhang, Shucheng J Multidiscip Healthc Original Research OBJECTIVE: The objective of this study is to reduce the dimension of several indicators with a strong correlation when conducting semen quality analysis in a small number of comprehensive variables that could retain most of the information in the original variables. METHODS: A total of 1132 subjects were recruited from the Maternal and Child Health Institutions of seven provinces in mainland China. They completed the questionnaire and provided semen samples. Visualization of the correlation between variables was realized by using a function chart and correlation in the PerformanceAnalytics package of the R programming language (version 3.6.3 [2020-02-29]). Factor analysis was conducted using the principal function in the psych package of R. Principal component analysis, combined with varimax rotation, was used in the operation of the model, and two common factors were selected and measured to provide values for the common factor. The score coefficient was estimated using the regression method. RESULTS: The contribution rates of the two common factors to variable X were 43.7% and 33.98%, respectively. When the two common factors were selected, approximately 78% of the information of the original variables could be explained. The correlation coefficients between the first common factor (the quantitative factor) and sperm density, total sperm count, and semen volume were 0.824, 0.984, and 0.544, respectively. The correlation coefficients between the second common factor (the quality factor) and sperm motility and the percentage of forward-moving (progressive spermatozoa) sperm were 0.978 and 0.976, respectively. CONCLUSION: The correlation between the original variables of a semen quality analysis was strong and suitable for dimensionality reduction by factor analysis. Factor analysis and dimensionality reduction provide a fast and accurate assessment of semen quality. Patients with low fertility or infertility can be identified and provided with corresponding treatments. Dove 2022-03-03 /pmc/articles/PMC8901226/ /pubmed/35264856 http://dx.doi.org/10.2147/JMDH.S341444 Text en © 2022 Wang et al. https://creativecommons.org/licenses/by-nc/3.0/This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/ (https://creativecommons.org/licenses/by-nc/3.0/) ). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php).
spellingShingle Original Research
Wang, Ning
Song, Meifang
Gu, Haike
Gao, Yiyuan
Yu, Ge
Lv, Fang
Shi, Cuige
Wang, Shangming
Sun, Liwen
Xiao, Yang
Zhang, Shucheng
A Factor Analysis Model for Rapid Evaluation of the Semen Quality of Fertile Men in China
title A Factor Analysis Model for Rapid Evaluation of the Semen Quality of Fertile Men in China
title_full A Factor Analysis Model for Rapid Evaluation of the Semen Quality of Fertile Men in China
title_fullStr A Factor Analysis Model for Rapid Evaluation of the Semen Quality of Fertile Men in China
title_full_unstemmed A Factor Analysis Model for Rapid Evaluation of the Semen Quality of Fertile Men in China
title_short A Factor Analysis Model for Rapid Evaluation of the Semen Quality of Fertile Men in China
title_sort factor analysis model for rapid evaluation of the semen quality of fertile men in china
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8901226/
https://www.ncbi.nlm.nih.gov/pubmed/35264856
http://dx.doi.org/10.2147/JMDH.S341444
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