An artificial neural network approach for classification of vector-borne diseases
Prajwal Shimpi, Sanskruti Shah, Maitri Shroff, Anand Godbole · 2017
Vector-Borne diseases are quite prevalent in India and cause a large number of deaths when they get aggravated, in turn leading to epidemics. It is quite easy to get infected by these diseases, which have very similar symptoms, most of which manifest after days. Technology, today, can provide a helping hand in the correct diagnosis of these diseases. In this paper, we take up three diseases prevalent in India: malaria, dengue and chikungunya. The proposed method uses an Artificial Neural Network (ANN) based backpropagation algorithm for training and testing. A number of gradient optimization techniques are used like Adaptive Moment Estimation, RMSProp, Adagrad, Classical Momentum and Nesterov accelerated gradient. The final probability of the most probable of the three diseases is given, based on the symptoms entered. Using backpropagation algorithm, an accuracy of 99.7% was achieved.