期刊名称：International Journal of Computer Science & Technology
印刷版ISSN：2229-4333
电子版ISSN：0976-8491
出版年度：2011
卷号：2
期号：2(Version 2)
出版社：Ayushmaan Technologies
摘要：Medical science industry has huge amount of data, but unfortunately most of this data is not mined to find out hidden information in data. Advanced data mining techniques can be used to discover hidden pattern in data. Models developed from these techniques will be useful for medical practitioners to take effective decision. In this research paper data mining classification techniques RIPPER classifier, Decision Tree, Artificial neural networks (ANNs), and Support Vector Machine (SVM) are analyzed on cardiovascular disease dataset. Performance of these techniques is compared through sensitivity, specificity, accuracy, error rate, True Positive Rate and False Positive Rate. In our studies 10-fold cross validation method was used to measure the unbiased estimate of these prediction models. As per our results error rates for RIPPER, Decision Tree, ANN and SVM are 02.756, 0.2755, 0.2248 and 0.1588 respectively. Accuracy of RIPPER, Decision Tree, ANN and SVM are 81.08%, 79.05%, 80.06% and 84.12% respectively. Our analysis shows that out of these four classification models SVM predicts cardiovascular disease with least error rate and highest accuracy.
关键词：heart disease; data mining techniques; RIPPER; decision tree;artificial neural networks; and support vector machine.?
