TY - JOUR
T1 - A Factor Analysis Model for Rapid Evaluation of the Semen Quality of Fertile Men in China
AU - Wang, Ning
AU - Song, Meifang
AU - Gu, Haike
AU - Gao, Yiyuan
AU - Yu, Ge
AU - Lv, Fang
AU - Shi, Cuige
AU - Wang, Shangming
AU - Sun, Liwen
AU - Xiao, Yang
AU - Zhang, Shucheng
N1 - Publisher Copyright:
© 2022 Wang et al.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Dimensionality reduction
KW - Factor analysis
KW - Score coefficient
KW - Semen quality analysis
UR - http://www.scopus.com/inward/record.url?scp=85125909042&partnerID=8YFLogxK
U2 - 10.2147/JMDH.S341444
DO - 10.2147/JMDH.S341444
M3 - Article
AN - SCOPUS:85125909042
SN - 1178-2390
VL - 15
SP - 431
EP - 441
JO - Journal of Multidisciplinary Healthcare
JF - Journal of Multidisciplinary Healthcare
ER -