Predicting seminal quality with artificial intelligence methods
(2012) In Expert Systems with Applications 39(16). p.12564-12573- 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. Artificial intelligence techniques are now an emerging methodology as decision support systems in medicine. In this paper we compare three artificial intelligence techniques, decision trees, Multilayer Perceptron and Support Vector Machines, in order to evaluate their performance in the prediction of the seminal quality from the data of the environmental factors and lifestyle. To do that we collect data by a normalized questionnaire from young healthy volunteers and then, we use the results of a semen analysis to asses the accuracy in the prediction of... (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. Artificial intelligence techniques are now an emerging methodology as decision support systems in medicine. In this paper we compare three artificial intelligence techniques, decision trees, Multilayer Perceptron and Support Vector Machines, in order to evaluate their performance in the prediction of the seminal quality from the data of the environmental factors and lifestyle. To do that we collect data by a normalized questionnaire from young healthy volunteers and then, we use the results of a semen analysis to asses the accuracy in the prediction of the three classification methods mentioned above. The results show that Multilayer Perceptron and Support Vector Machines show the highest accuracy, with prediction accuracy values of 86% for some of the seminal parameters. In contrast decision trees provide a visual and illustrative approach that can compensate the slightly lower accuracy obtained. In conclusion artificial intelligence methods are a useful tool in order to predict the seminal profile of an individual from the environmental factors and life habits. From the studied methods, Multilayer Perceptron and Support Vector Machines are the most accurate in the prediction. Therefore these tools, together with the visual help that decision trees offer, are the suggested methods to be included in the evaluation of the infertile patient. (C) 2012 Elsevier Ltd. All rights reserved. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/3146743
- author
- Gil, David ; Luis Girela, Jose ; De Juan, Joaquin ; Jose Gomez-Torres, M. and Johnsson, Magnus LU
- organization
- publishing date
- 2012
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Artificial neural network, Support Vector Machines, Decision trees, Diagnosis, Decision support system, Expert system, Semen quality, Male, fertility potential
- in
- Expert Systems with Applications
- volume
- 39
- issue
- 16
- pages
- 12564 - 12573
- publisher
- Elsevier
- external identifiers
-
- wos:000307796300028
- scopus:84864494677
- ISSN
- 0957-4174
- DOI
- 10.1016/j.eswa.2012.05.028
- language
- English
- LU publication?
- yes
- id
- a53191fb-fa38-4d82-8f8a-d382253e0915 (old id 3146743)
- date added to LUP
- 2016-04-01 10:43:07
- date last changed
- 2022-03-04 22:12:27
@article{a53191fb-fa38-4d82-8f8a-d382253e0915,
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. Artificial intelligence techniques are now an emerging methodology as decision support systems in medicine. In this paper we compare three artificial intelligence techniques, decision trees, Multilayer Perceptron and Support Vector Machines, in order to evaluate their performance in the prediction of the seminal quality from the data of the environmental factors and lifestyle. To do that we collect data by a normalized questionnaire from young healthy volunteers and then, we use the results of a semen analysis to asses the accuracy in the prediction of the three classification methods mentioned above. The results show that Multilayer Perceptron and Support Vector Machines show the highest accuracy, with prediction accuracy values of 86% for some of the seminal parameters. In contrast decision trees provide a visual and illustrative approach that can compensate the slightly lower accuracy obtained. In conclusion artificial intelligence methods are a useful tool in order to predict the seminal profile of an individual from the environmental factors and life habits. From the studied methods, Multilayer Perceptron and Support Vector Machines are the most accurate in the prediction. Therefore these tools, together with the visual help that decision trees offer, are the suggested methods to be included in the evaluation of the infertile patient. (C) 2012 Elsevier Ltd. All rights reserved.}},
author = {{Gil, David and Luis Girela, Jose and De Juan, Joaquin and Jose Gomez-Torres, M. and Johnsson, Magnus}},
issn = {{0957-4174}},
keywords = {{Artificial neural network; Support Vector Machines; Decision trees; Diagnosis; Decision support system; Expert system; Semen quality; Male; fertility potential}},
language = {{eng}},
number = {{16}},
pages = {{12564--12573}},
publisher = {{Elsevier}},
series = {{Expert Systems with Applications}},
title = {{Predicting seminal quality with artificial intelligence methods}},
url = {{http://dx.doi.org/10.1016/j.eswa.2012.05.028}},
doi = {{10.1016/j.eswa.2012.05.028}},
volume = {{39}},
year = {{2012}},
}