Prediction of Trypanosoma evansi infection in dromedaries using artificial neural network (ANN)

Abstract

Surra is caused by Trypanosoma evansi, a flagellated parasite that affects domestic and wild animals. Surra is a neglected tropical disease causing serious problems to camels breed in Algeria. The aim of our study consists to extract the major risk factors that predict T.evansi infection in dromedaries using artificial neural networks. This investigation was conducted on 115 dromedaries from Ghardaïa district, Southern Algeria. The immune trypanolysis test was used to detect antibodies against T. evansi. Firstly, the gamma test has been used to choose optimal input parameters. The obtained results indicate that the age, gender, breed, clinical manifestations history, herd size, as well as the animal activities were the most predictors of T. evansi infection. Afterward, an artificial neural network method has been performed for modelling the proposed optimal inputs and their accuracy was assessed through seven statistical indicators. The comparative study indicates the effectiveness of the (6−9−1) model trained by the Tansig transfer function. The proposed model has demonstrated a good performance: 0.925 for training data and 0.962 for validation data. Furthermore it could be very useful for the rapid intervention of veterinarians as close as possible to the point-of-care (POC).