Classification of Acute Upper Gastrointestinal Bleeding based on Rough Neural Network

Abstract

The main emphasize of paper is the classification of Acute upper Gastrointestinal Bleeding based on Rough neural network. Classification is used to extract model describing important and data classes or future data trends. A conventional neural network consists of several layers of neurons. Each neuron receives input from other neurons and external environment and produces output. A rough neural network consists of conventional neurons and rough neurons connected to each other. A rough neuron can be viewed as a pair of neurons, one for the upper bound and the other for the lower bound. Rough neural network consists of one input layer, one output layer and one hidden layer. The system can classify 7 types of classes for acute upper gastrointestinal bleeding. This system is implemented by using Java programming language.