Hybrid Classifier for Identification of Heart Disease

Cardiovascular disease has increased rapidly in the past few decades. It has become a leading cause of death globally. Heart disease has affected the global heterogeneous population irrespective of age and gender. According to World Health Organization, an estimated 17.3 million people died from cardiovascular diseases in 2008, representing 30% of all global deaths. The accurate and timely prediction of these diseases has become a challenge for medical organizations. A mere assumption of absence or presence of disease is an approach used by many hospitals to give prediction results. The predictions of the heart disease are dependent mainly on the prominent factors involved and their effect weightage. Finding out the patterns and extracting knowledge from those patterns is the major task at hand. Data mining techniques have proven to be a good means for this knowledge discovery. This study makes use of the prominent features of two data mining techniques, namely, K-Means Clustering and Decision Tree. These methods, one being unsupervised learning and the other supervised learning, use very different approaches to predict the results. The positive factors of both the techniques have been used to build a Hybrid Classifier. The aim is to provide an algorithm which gives the best accuracy and performance for the Heart disease identification system.