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

In English

Författare

  • Jose L. Girela
  • David Gil
  • Magnus Johnsson
  • Maria Jose Gomez-Torres
  • Joaquin De Juan

Summary, in English

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.

Publiceringsår

2013

Språk

Engelska

Sidor

99-99

Publikation/Tidskrift/Serie

Biology of Reproduction

Volym

88

Issue

4

Dokumenttyp

Artikel i tidskrift

Förlag

Oxford University Press

Ämne

  • Computer Vision and Robotics (Autonomous Systems)

Nyckelord

  • artificial neural network
  • decision support system
  • life habits
  • semen
  • quality
  • supervised learning

Status

Published

ISBN/ISSN/Övrigt

  • ISSN: 1529-7268