An intelligent model for prediction of brucellosis

Malta fever or brucellosis is one of the most common diseases of humans and animals. Bacteria from Brucella family can infect most living creatures like cows, sheep, goats, humans, horses, dogs and the like. This disease is important because humans are constantly in contact with livestock or animal products such as dairy products, animal meat, etc. Data mining is the process of extracting hidden information or patterns and relationships in a large volume of data in one or more large databases. Such data can be used to increase revenue and reduce costs. By data mining techniques, it is possible to use prognostic models to detect livestock with brucellosis and to reduce the complications of the disease. The purpose of this study was to evaluate the accuracy of prediction and diagnosis of livestock with brucellosis disease using neural network algorithm and decision tree. To perform this study, appropriate MATLAB software was used. Investigations indicated that the decision tree provided predictions with higher accuracy.