Medical Diagnosis using Back Propagation Algorithm in ANN

Artificial Neural Networks are finding many uses in the Medical diagnosis applications. ANN plays a vital role in the medical field in solving various health problems like acute diseases and other mild diseases. The goal of this paper is to evaluate Artificial Neural Network in disease diagnosis. Three cases are studied. The first one is diabetes disease, data is the risk factors and their strength of association to the development of type 2 diabetes was used as relative weight of input variables. The second is the Hypertension disease, data is disease symptoms. Third is obesity disease i.e. body fat, data is disease symptoms. In all the above mentioned three diseases each patient is classified into two categories infected and non- infected. Classification and Prediction are important tool in medical diagnosis decision support. Feed Forward Back Propagation Neural Network Model is used as classifier to distinguish between infected and non-infected persons in all cases. In this study, the data were obtained from UCI machine learning repository in order to diagnosed diseases. The data is separated into inputs and targets. The targets for the neural network will be identified with 1's as infected and will be identified with 0's as non-infected. The Back- Propagation neural network model is systematically trained and with data sets.