ANALYSIS OF CLASSIFICATION ALGORITHMS USING HEART DISEASES DATA SET FOR PREDICTION ITS ACCURACIES

Abstract

Heart disease is the very important role for human death and we predict it at earlier stage to save the human life. So many of classification algorithms available in the data mining, we selected as few classification algorithms for heart disease prediction and found the accuracies. Different algorithms give various levels of accuracies. In the paper comparing the accuracies of few classification algorithms are Random Tree,Naives Bayes,Decision Tree and Random Forest then used K-Means clustering. The hungarian_csv, cleveland.csv and switzerland.csv heart disease data set received from UCI repository with 1272 instance and 14 regular attributes age, sex,cp, restbps, chol,fbs,restecg, thalach, exang, oldpeak , slope, ca, thalm, num were used here for analysis. Rapid miner studio software is a data science software platform developed by the company of the same name that provides an integrated environment for machine learning, data mining predicate analytics and business analysis. The different measures and result were tabulated and charted.