ENSEMBLE ONLINE SEQUENTIAL EXTREME LEARNING MACHINE AND SWARM INTELLIGENT BASED FEATURE SELECTION FOR CLEVELAND HEART DISEASE PREDICTION SYSTEM

In Healthcare industries generally clinical diagnosis is done mostly by doctor's expertise and experience. Computer Aided Decision Support System plays a major role in medical field. With the growing research on heart disease predicting system, it has become important to categories the research outcomes and provides readers with an overview of the existing heart disease prediction techniques in each category. The purpose of this paper is to develop a cost effective treatment using data mining Ensemble of Online Sequential Extreme Learning Machine (EOS-ELM) for facilitating data base decision support system. Heart disease patient database is collected from Cleveland Heart Disease Dataset (CHDD) available on the University of California, Irvine (UCI) Repository. Since the database samples consists of huge attribute and selection of best attribute from this CHDD becomes very important for prediction accuracy. So the dimensionality reduction is done by using Modified Genetic Algorithm (MGA) from these features set significantly speeds up the prediction task. This research paper added two more attributes i.e. obesity and smoking. The results from experiments returned with diminishing fact that there is considerable improvement in classification and prediction. The proposed EOS-ELM works as promising tool for prediction of heart disease when compared to other data mining classification techniques, namely Naïve Bayes (NB), Decision Tree (DT) and Artificial Neural Networks (ANN) and are analyzed on Cleveland Heart Disease Database (CHDD). The system designed in MATLAB software can be viewed as an alternative for existing methods to distinguish of heart disease presence