Risk Level Prediction for Heart Disease using Decision Tree Induction

Heart Disease was the major cause of causalitiesin most of the countries. According to the medicalrecords, heart disease kills one person in very sorttime. Classification and prediction are the forms ofdata analysis that can be used to extract models forimportant classes or to predict future data trends. Inthis paper, decision tree induction algorithm is usedto classify the risk level for heart disease. Decisiontree is a flow-chart-like tree structure, where eachinternal node denotes a test on an attributes, eachbranch represents an outcome of the test, and theleaf nodes represent classes or class distributions.This system generates the understandable rules foruser and estimates the accuracy for classifier.Depending on the attribute values of the data set,this system can classify the risk level of heartdisease whether it is in serious or normal conditionsfor patients. Thus, the user can test his or hermedical check concerned with their heart.Moreover, the system can provide the classifieraccuracy by using Holdout Method