Predicting seminal quality with artificial intelligence methods
نویسندگان
چکیده
0957-4174/$ see front matter 2012 Elsevier Ltd. A http://dx.doi.org/10.1016/j.eswa.2012.05.028 ⇑ Corresponding author. E-mail address: [email protected] (D. Gil). 1 These authors equally contributed to this work. 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. 2012 Elsevier Ltd. All rights reserved.
منابع مشابه
The Efficacy of In Vitro Sperm Tests in Predicting Pregnancy Success after Artificial Insemination in the Bitch
In many species, including the dog (Canis familiaris), semen analysis protocols has improved in recent years. In vitro assessment of semen quality provides important information; however, the ultimate parameter for determining semen quality is pregnancy outcome. The objective of the present study was to investigate the efficiency of in vitro sperm tests in predicting seminal quality in dogs by ...
متن کاملTitle: SEMEN PARAMETERS CAN BE PREDICTED FROM ENVIRONMENTAL FACTORS AND LIFESTYLE USING ARTIFICIAL INTELLIGENCE METHODS Short Title: Predicting Semen Quality with Neural Networks
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 from environmental factors, life habits and health status, as a possible Decision Support System that can help in...
متن کاملAdaptive Network-based Fuzzy Inference System-Genetic Algorithm Models for Prediction Groundwater Quality Indices: a GIS-based Analysis
The prediction of groundwater quality is very important for the management of water resources and environmental activities. The present study has integrated a number of methods such as Geographic Information Systems (GIS) and Artificial Intelligence (AI) methodologies to predict groundwater quality in Kerman plain (including HCO3-, concentrations and Electrical Conductivity (EC) of groundwater)...
متن کاملSpermatozoa Molecules in Relation to Bulls Fertility
Bull fertility may be defined as the process by which spermatozoa fertilize and activate the ovum and then support embryonic development. Bull fertility is a complex trait having relatively low heritability and plays a vital role for efficient production and reproduction of bovine. Various mechanisms involved in regulating bull fertility associated phenotype and reliable biomarkers are poorly d...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- Expert Syst. Appl.
دوره 39 شماره
صفحات -
تاریخ انتشار 2012