Classification using Convolutional Neural Network for Heart and Diabetics Datasets

Abstract

The neural network approach to generate efficient classification rules. Convolution neural network algorithm is a multilayer perceptron that is the special design for identification of two-dimensional data information. Always have more layers: input layer, convolution layer, sample layer and output layer. Deep learning refers to the shining branch of machine learning that is based on learning levels of representations. Convolutional Neural Networks (CNN) is one kind of deep neural network. To perform classification task of heart disease dataset, the neural network is trained using convolutions algorithm. The experiment is conducted with heart disease dataset by considering the single and multilayer neural network modes. The proposed algorithm gives detailed analysis of the process of CNN algorithm both the forward process and back propagation. Then we applied improved convolutional neural network to implement the typical heartdata recognition using weka tool. The experimental result show the best classification accuracy compare with existing classification algorithm.