期刊名称：International Journal of Information Technology Convergence and Services (IJITCS)
印刷版ISSN：2231-1939
电子版ISSN：2231-153X
出版年度：2011
卷号：1
期号：4
出版社：AIRCC
摘要：In this paper empirical comparison is carried out with various supervised algorithms. We studied the performance criterion of the machine learning tools such as Naïve Bayes, Support vector machines, Radial basis neural networks, Decision trees J48 and simple CART in detecting diseases. We used both binary and multi class data sets namely WBC, WDBC, Pima Indians Diabetes database and Breast tissue from UCI machine learning depositary. The experiments are conducted in WEKA. The aim of this research is to find out the best classifier with respect to disease detection.
