Detection of Breast Cancer using MLP and RBF Classifiers
Published: 2010
Author(s) Name: C.Meenakshi, M.Govindarajan, A.M.Sameeullah
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Abstract
Data mining is the use of algorithms to extract the information and patterns derived by the knowledge discovery from databases. Classification maps data into predefined groups or classes. It is often referred to as supervised learning because the classes are determined before examining the data. The prognosis and diagnosis of cancer has been a challenging research problem for many researchers. The main objective of this proposed work is to compare the performance analysis of various data mining techniques to identify the breast cancer prognosis. This work employs two different kinds of neural network classifiers: the multilayer perceptron (MLP) and the radial basis function (RBF). It demonstrated the classification accuracy of MLP classifier is 79.20 % and radial basis function classifier is 77.78 %. It confirms that the MLP networks produce more specific, accurate results compared to RBF.
Keywords : Data Mining, Classification, Multi Layer Perceptron, Radial Basis Function and Classification Accuracy.
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