A neuro-genetic model to predict hepatitis disease risk

Abstract

In the present scenario, large quantity of data is generated in the field of medicine. This data contain valuable information which can be utilized in decision making. Machine learning is an active area which may be useful to healthcare experts. Hepatitis disease is a common disease in the world, which may cause damage to hepatocytes. Machine learning techniques can be implemented to reduce the risk of Hepatitis. Our study has demonstrated an intelligent hybrid system for the efficient risk prediction of Hepatitis disease. We developed an intelligent combination of Genetic search algorithm and Multilayer Perceptron technique named MLP-GS. Our proposed system model was analyzed and computed with the help of several performance parameters like Accuracy, Root Mean-Squared Error, Precision, Recall and F-Measure. It was observed that MLP-GS model performs better on Hepatitis data.