Hybrid approach for data classification in e-health cloud

Abstract

The growth of IT industry technology is absorbed by the cloud service technology, which leads to secure connectivity and availability of services to cloud users. This paper proposes an e-health data classification system that gives the services to e-health cloud user for their disease prediction requirements. The tree bagging method is suitable to select better weighted attributes and the careful seeding of K-means ++ algorithm improves the accuracy and speed of clustering. Most of the tree classification methods use only information gain as the strategy to select suitable attributes for classification. We used information gain in bagging technique to improve accuracy. In this research article, we proposed a method to Coalescing Decision tree classification algorithm, bagging technique, and K-means++ algorithm to build a better classifier for the e-health cloud users. It was evaluated with the standard data sets such as Diabetes, breast cancer, liver disorders and cardiotocography.