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Semen Parameters Can Be Predicted from Environmental Factors and Lifestyle Using Artificial Intelligence Methods

Girela, Jose L.; Gil, David; Johnsson, Magnus LU ; Gomez-Torres, Maria Jose and De Juan, Joaquin (2013) In Biology of Reproduction 88(4). p.99-99
Abstract
Fertility rates have dramatically decreased in the last two decades, especially in men. It has been described that environmental factors as well as life habits may affect semen quality. In this paper we use artificial intelligence techniques in order to predict semen characteristics resulting from environmental factors, life habits, and health status, with these techniques constituting a possible decision support system that can help in the study of male fertility potential. A total of 123 young, healthy volunteers provided a semen sample that was analyzed according to the World Health Organization 2010 criteria. They also were asked to complete a validated questionnaire about life habits and health status. Sperm concentration and... (More)
Fertility rates have dramatically decreased in the last two decades, especially in men. It has been described that environmental factors as well as life habits may affect semen quality. In this paper we use artificial intelligence techniques in order to predict semen characteristics resulting from environmental factors, life habits, and health status, with these techniques constituting a possible decision support system that can help in the study of male fertility potential. A total of 123 young, healthy volunteers provided a semen sample that was analyzed according to the World Health Organization 2010 criteria. They also were asked to complete a validated questionnaire about life habits and health status. Sperm concentration and percentage of motile sperm were related to sociodemographic data, environmental factors, health status, and life habits in order to determine the predictive accuracy of a multilayer perceptron network, a type of artificial neural network. In conclusion, we have developed an artificial neural network that can predict the results of the semen analysis based on the data collected by the questionnaire. The semen parameter that is best predicted using this methodology is the sperm concentration. Although the accuracy for motility is slightly lower than that for concentration, it is possible to predict it with a significant degree of accuracy. This methodology can be a useful tool in early diagnosis of patients with seminal disorders or in the selection of candidates to become semen donors. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
artificial neural network, decision support system, life habits, semen, quality, supervised learning
in
Biology of Reproduction
volume
88
issue
4
pages
99 - 99
publisher
Soc Study Reproduction
external identifiers
  • wos:000318490800005
  • scopus:84877011978
ISSN
1529-7268
DOI
10.1095/biolreprod.112.104653
language
English
LU publication?
yes
id
e5cae30b-2a86-4009-851d-8ef53b6150fc (old id 3821612)
date added to LUP
2013-06-25 11:22:19
date last changed
2019-04-23 01:31:51
@article{e5cae30b-2a86-4009-851d-8ef53b6150fc,
  abstract     = {Fertility rates have dramatically decreased in the last two decades, especially in men. It has been described that environmental factors as well as life habits may affect semen quality. In this paper we use artificial intelligence techniques in order to predict semen characteristics resulting from environmental factors, life habits, and health status, with these techniques constituting a possible decision support system that can help in the study of male fertility potential. A total of 123 young, healthy volunteers provided a semen sample that was analyzed according to the World Health Organization 2010 criteria. They also were asked to complete a validated questionnaire about life habits and health status. Sperm concentration and percentage of motile sperm were related to sociodemographic data, environmental factors, health status, and life habits in order to determine the predictive accuracy of a multilayer perceptron network, a type of artificial neural network. In conclusion, we have developed an artificial neural network that can predict the results of the semen analysis based on the data collected by the questionnaire. The semen parameter that is best predicted using this methodology is the sperm concentration. Although the accuracy for motility is slightly lower than that for concentration, it is possible to predict it with a significant degree of accuracy. This methodology can be a useful tool in early diagnosis of patients with seminal disorders or in the selection of candidates to become semen donors.},
  author       = {Girela, Jose L. and Gil, David and Johnsson, Magnus and Gomez-Torres, Maria Jose and De Juan, Joaquin},
  issn         = {1529-7268},
  keyword      = {artificial neural network,decision support system,life habits,semen,quality,supervised learning},
  language     = {eng},
  number       = {4},
  pages        = {99--99},
  publisher    = {Soc Study Reproduction},
  series       = {Biology of Reproduction},
  title        = {Semen Parameters Can Be Predicted from Environmental Factors and Lifestyle Using Artificial Intelligence Methods},
  url          = {http://dx.doi.org/10.1095/biolreprod.112.104653},
  volume       = {88},
  year         = {2013},
}