A Comparison of the Efficiency of Heart Disease Data Classification using Data Mining Technique

Abstract

The purposes of the research were to 1) to compare the technical features, select the most suitable properties 2) study the experimental results using RapidMiner Studio program, consisting of condensed calculation techniques, 3 most techniques, simple ben, neural net by testing with the heart 303 people The research findings showed that the comparison of the efficiency of the average data classification was Nerve net with the most accuracy of 82.17%, followed by Naïve Bayesian 81.88% and KNearest Neighbor 65.66%. By using only 11 features. With Nerve net technology being the most accurate and the performance comparison shows that the selection of appropriate characteristics before data classification is an appropriate way to enhance the efficiency of data classification. Therefore, it is very necessary to first filter the data.