A Comparative Study of Multilayer Perceptron, Radial Basis Function Networks and logistic Regression for Healthcare Data Classification

Abstract

The Healthcare databases are becoming more important nowadays. Many Healthcare institutions are
maintaining the large volume of healthcare databases to provide the best clinical services and insurance claims. The
profits of Healthcare insurance companies are totally depending on the care of their customers. It is predicted by the
healthcare department of United States of America that the early detection of any disease and its cause is very important
strategy to save the big amount of insurance claim. Therefore Healthcare data classification approach has become the
dominant process to save the big amount of budget allocation for the government sector. There are many types of
classification approaches used for classification and prediction. In this research paper mainly multilayered Perceptron,
Radial basis function networks and Logistic Regression are used to classify the Healthcare databases and on the basis of
classification trends the decision are taken. All these approaches of data classification are covered. in this paper.