Prediction of Endocrine System Affectation in Fisher 344 Rats by Food Intake Exposed with Malathion, Applying Naïve Bayes Classifier and Genetic Algorithms

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 Naïve
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.

Keywords: Artificial intelligence, machine learning, organophosphate, rat