ANALYSIS OF HEART DISEASES DATASET USING NEURAL NETWORK APPROACH (JAVA)

In this experiment the neural network is trained with Heart Diseases database by using feed forward neural network model and backpropagation learning algorithm with momentum and variable learning rate. The input layer of the network consists of 13 neurons to represent each attribute as the database consists of 13 attributes. The number of classes are four: 0 – normal person,1 - first stroke, 2- second stroke and 3- end of life. The output layer consists two neurons to represent these four classes. The description of the backpropagation algorithm is specified in the above is used to train the neural network during the training process. Several neural networks are constructed with and without hidden layers, i.e, single and multi layer networks and trained with heart disease dataset.