Prediction of Reproductive System Affectation in Sprague Dawley Rats by food Intake Exposed with Fenthion, using Naïve Bayes Classifier and Genetic Algorithms

Juan David Sandino, Dario Amaya Hurtado and Olga Lucia Ramos

Virtual Applications Group-GAV, Universidad Militar Nueva Granada –UMNG.

Corresponding Author E-mail: u1801731@unimilitar.edu.co

DOI : http://dx.doi.org/10.13005/bbra/2572

ABSTRACT: Improper application of pesticides in agricultural crops and indirect effects caused by exposure to them through consumption of contaminated crops, nowadays represent a serious risk to public health harmony. It is vital then, to know the degree of toxicity of each of these chemicals in order to properly regulate its application and sensitize the population at risk. Therefore, this paper shows the results of an algorithm with the ability to predict the effects on the reproductive system in Sprague Dawley rats, caused by the intake of food exposed with Fenthion. The original data were processed using the Naïve Bayes classifier, then optimized using genetic algorithms. It is concluded that the prediction algorithm does the job properly, processing qualitative information with relatively low computational cost, which allows its easy portability to different development platforms.

KEYWORDS: Artificial Intelligence; Machine Learning; Organophosphate

[ HTML Full Text]