prediction of endocrine system affectation in fisher 344 rats by food intake exposed with malathion, applying naïve bayes classifier and genetic algorithms

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ID: 150918
2016
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Abstract
Background: Reported cases of uncontrolled use of pesticides and its produced effects by direct or indirect exposition, represent a high risk for human health. Therefore, in this paper, it is shown the results of the development and execution of an algorithm that predicts the possible effects in endocrine system in Fisher 344 (F344) rats, occasioned by ingestion of malathion. Methods: It was referred to ToxRefDB database in which different case studies in F344 rats exposed to malathion were collected. The experimental data were processed using Naοve Bayes (NB) machine learning classifier, which was subsequently optimized using genetic algorithms (GAs). The model was executed in an application with a graphical user interface programmed in C#. Results: There was a tendency to suffer bigger alterations, increasing levels in the parathyroid gland in dosages between 4 and 5 mg/kg/day, in contrast to the thyroid gland for doses between 739 and 868 mg/kg/day. It was showed a greater resistance for females to contract effects on the endocrine system by the ingestion of malathion. Females were more susceptible to suffer alterations in the pituitary gland with exposure times between 3 and 6 months. Conclusions: The prediction model based on NB classifiers allowed to analyze all the possible combinations of the studied variables and improving its accuracy using GAs. Excepting the pituitary gland, females demonstrated better resistance to contract effects by increasing levels on the rest of endocrine system glands.
Reference Key
mora2016internationalprediction Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Juan David Sandino Mora;Darío Amaya Hurtado;Olga Lucía Ramos Sandoval
Journal italian journal of animal science
Year 2016
DOI
10.4103/2008-7802.190611
URL
http://www.ijpvmjournal.net/article.asp?issn=2008-7802;year=2016;volume=7;issue=1;spage=111;epage=111;aulast=Mora
Keywords
artificial intelligence Machine learning organophosphate ratmedicine

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