Prediction and Classification Of Vector-Borne and Communicable Diseases through Artificial Neural Networks

Abstract

It is easy enough to be infected with communicable and vector-borne diseases, which have very similar symptoms, most of which occur after days. Nowadays technology can help in the correct diagnosis of these diseases. Early diagnosis is necessary to ensure that appropriate treatments and medications are administered, which requires the need for an automated system to predict possible infections. This requires a system that allows the patient to distinguish between these conditions and diagnose the possible disease based on symptoms. After having diagnosed the disease, the goal is to provide appropriate treatment based on the type of disease expected. The implementation of this medical diagnosis system is carried out with the help of Artificial Neural Networks that use backpropagation algorithm for training. With the implementation of Artificial Neural Networks in medical diagnosis, the accuracy of the system improves with respect to the rule-based model and with the use of the backpropagation algorithm together with the gradient optimization technique, the results are more precise.