Breast cancer has become the most common cause of death in women. Early detection of breast cancer helps out to reduce the risk factors. Three classification algorithms (NB, DT, and KNN) were used on two different Breast cancer datasets using the WEKA tool. The main purpose of this paper is to compare the results of the classification algorithms using voting and feature selection methods. The experimental result shows that voting of three classifiers gives the highest performance accuracy on the Breast cancer dataset. The ensemble method is used to increase the accuracy of the data mining algorithms. We also compare the performance accuracy of classifiers using feature selection methods (IG and PCA) on breast cancer datasets.

J48,NaïveBayes,KNN,Voting classifier, feature selection

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