This document compares the performance of several data mining classification techniques (Naive Bayes, SVM, K-Star, J48, Random Forest) in predicting heart disease using a heart disease dataset from the UCI repository. It finds that SVM achieved the highest accuracy of 84.07% at predicting heart disease risk levels (high, medium, low) using attributes like age, sex, blood pressure readings from the dataset. The document provides background on data mining and knowledge discovery from databases, describes the different classification techniques used, and discusses related work analyzing heart disease prediction using data mining tools like WEKA.