A Wrapper Feature Selection Technique for Improving Diagnosis of Breast Cancer

Document Type : Original Research Articles.

Authors

1 Faculty of computers and information systems , I.S dep. Mansoura University, Egypt

2 Faculty of computers and information systems , I.T dep. Mansoura University, Egypt

Abstract

 Nowadays, cancer is considered as a fairly common disease. Regarding the number of newly detected cases, breast cancer is ranked as one of the most leading cancer types to death in women. It can be cured, if it is identified and treated in its early stages. Therefore, this study explores a proposed integrated wrapper feature selection method called wrapper
naïve-greedy search (WNGS) to improve the accuracy of the breast cancer diagnosis. WNGS is based on a wrapper
method, which is blended with a greedy forward search to select optimal feature subset. WNGS method integrates a
wrapper method based on Naïve Bayes (NB) classifier as a learning scheme with a forward greedy search method. Then,
the selected feature subset is fed to a classifier to determine breast cancer. In addition, K-nearest neighbor-greedy search
(KNN-GS) is used for comparison. In KNN-GS method, knearest neighbor (KNN) classifier is used as a learning
scheme while a forward greedy search method is used to search through features. NB is used as the classifier for
classification process for both methods. By applying these two methods, data features are reduced, and the classification
rate is improved. Both methods are tested on two different benchmark breast cancer datasets. Accuracy results showed
that WNGS method outperformed KNN-GS method. Also, WNGS method overcame KNN-GS regarding precision,
recall, F-measure, and sensitivity. 

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Mansoura Journal for Computer and Information Sciences
Volume 13, Issue 1
December 2017
Pages 43-49

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