Semen parameters can be predicted from environmental factors and lifestyle using artificial intelligence methods
Please use this identifier to cite or link to this item:
http://hdl.handle.net/10045/35759
Title: | Semen parameters can be predicted from environmental factors and lifestyle using artificial intelligence methods |
---|---|
Authors: | Girela, Jose L. | Gil, David | Johnsson, Magnus | Gómez-Torres, María José | Juan Herrero, Joaquín de |
Research Group/s: | Biotecnología | Lucentia |
Center, Department or Service: | Universidad de Alicante. Departamento de Biotecnología | Universidad de Alicante. Departamento de Tecnología Informática y Computación |
Keywords: | Semen quality | Life habits | Supervised learning | Artificial neural network | Decision support system |
Knowledge Area: | Biología Celular | Arquitectura y Tecnología de Computadores |
Issue Date: | 27-Feb-2013 |
Publisher: | Society for the Study of Reproduction |
Citation: | Biol Reprod April 2013 88 (4) 99, 1-8; published ahead of print February 27, 2013, doi:10.1095/biolreprod.112.104653 |
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. |
Sponsor: | This study was partially funded by Vicerrectorado de Investigación, University of Alicante, Alicante, Spain (Vigrob-137). |
URI: | http://hdl.handle.net/10045/35759 |
ISSN: | 0006-3363 (Print) | 1529-7268 (Online) |
DOI: | 10.1095/biolreprod.112.104653 |
Language: | eng |
Type: | info:eu-repo/semantics/article |
Rights: | Copyright © 2013 by the Society for the Study of Reproduction |
Peer Review: | si |
Publisher version: | http://dx.doi.org/10.1095/biolreprod.112.104653 |
Appears in Collections: | INV - GIDBT - Artículos de Revistas INV - LUCENTIA - Artículos de Revistas Institucional - IUIEG - Publicaciones |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
2013_Girela_etal_BOR.pdf | Versión revisada (acceso abierto) | 756,09 kB | Adobe PDF | Open Preview |
Items in RUA are protected by copyright, with all rights reserved, unless otherwise indicated.