Prediction of Endocrine System Affectation in Fisher 344 Rats by Food Intake Exposed with Malathion, Applying Naïve Bayes Classifier and Genetic Algorithms.
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.
Department of Mechatronics Engineering, Faculty of Engineering, Nueva Granada Military University, Bogotá, Colombia.
Juan David Sandino
JD
Mora
Department of Mechatronics Engineering, Faculty of Engineering, Nueva Granada Military University, Bogotá, Colombia.
Darío Amaya
DA
Hurtado
Department of Mechatronics Engineering, Faculty of Engineering, Nueva Granada Military University, Bogotá, Colombia.
Olga Lucía Ramos
OL
Sandoval
Artificial intelligence; machine learning; organophosphate; rat