Ensembled Rule Based Classification Algorithms for predicting Imbalanced Kidney Disease Data

Abstract

Imbalanced data is a type of data where there exists a difference in the ratio of classes. It occurs easily in real life of data analysis. In Data mining the functioning of learning algorithms caused by the imbalanced data. Most of the machine learning algorithms has a tendency to prejudice towards the class of majority in case of imbalanced data and hence those algorithms misjudge the minority class. Therefore, In this article we discuss a systematic way to address the imbalanced data classification problem by applying the rule based ensemble learning techniques like bagging, boosting, voting and stacking to build models, and then accelerates the performance of learning algorithms. In this research, we have preferred real data of chronic kidney disease which is collected from Appolo Hospitals, Tamil Nadu, India, to predict kidney disease of patients .The collected data is initially imbalanced. Firstly, the imbalanced data is balanced by applying SMOTE algorithm, which is an over sampling technique. Then applied various ensemble learning techniques to make better prediction. The incurred results showed that the model template chosen can minimize the problem of misclassification of imbalanced data efficaciously. But this model template cannot classify correctly when imbalanced rate of class increases i.e. in case of Big Data. For better result of imbalanced Big Data, new algorithmic plan of action has to be exploited which can be measured by using Hadoop framework and mapreduce programming model.