DATA MINING FOR HEART DISEASE PREDICTION SYSTEM

Abstract

Data mining which is also known as knowledge discovery is the process in which we extract useful information from the large set of the data. Use data mining classification modeling technique, namely, decision trees. In data mining technique the decision tree approach is more powerful for classification problems. A decision tree made of a root node, branches and leaf nodes. To evaluate the data, follow the path from root node to reach leaf node. There are two steps in this techniques building a tree & applying the tree to the dataset. There are many popular decision tree algorithms CART, ID3, C4.5, CHAID, and J48. This technique gives maximum accuracy on training data. The overall concept is to build a tree that provides balance of flexibility & accuracy.  
Heart disease leading cause of death in the world over the past 10 years, about 25% of deaths in the age group of 25-69 years occur because of heart disease. Heart disease is major disease all over world. In medical science prediction of heart disease is very important. Applying decision tree technique to heart disease prediction data can provide as reliable performance to achieve in diagnosing heart disease. In data mining, decision tree techniques are used to find heart disease of patient. Based on the risk factors the heart disease can predict.