A hybrid data mining classifier for breast cancer prediction

Abstract

Classification and data mining methods are an effective way to classify data, especially in medical field, where those methods are widely used in diagnosis and analysis to make decisions. This paper presents a performance comparison between different machine learning algorithms: Support Vector Machine (SVM), Decision Tree (C4.5), Naive Bayes (NB) and k Nearest Neighbors (k-NN) applied to the Wisconsin Breast Cancer (WBC original) datasets. We use classification accuracy and confusion matrix based on 10-fold cross validation method. We also introduce a fusion at classification level between those classifiers to get the most accurate multi-classifiers approach. Experimental results show that the classification using fusion of SVM, NB and C4.5 reached the highest accuracy (97.31%) while accuracy of using a single classifier SVM is (97.13%). All experiments are executed within a simulation environment and conducted in WEKA data mining tool.