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Computational and Mathematical Methods in Medicine
Volume 2015, Article ID 265138, 11 pages
http://dx.doi.org/10.1155/2015/265138
Research Article

Breast Cancer Detection with Reduced Feature Set

1Department of Electrical and Electronics, Piri Reis University, 34940 Istanbul, Turkey
2Department of Electrical and Electronics, Istanbul University, 34320 Istanbul, Turkey

Received 12 September 2014; Revised 14 December 2014; Accepted 25 December 2014

Academic Editor: Kevin Ward

Copyright © 2015 Ahmet Mert et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper explores feature reduction properties of independent component analysis (ICA) on breast cancer decision support system. Wisconsin diagnostic breast cancer (WDBC) dataset is reduced to one-dimensional feature vector computing an independent component (IC). The original data with 30 features and reduced one feature (IC) are used to evaluate diagnostic accuracy of the classifiers such as k-nearest neighbor (k-NN), artificial neural network (ANN), radial basis function neural network (RBFNN), and support vector machine (SVM). The comparison of the proposed classification using the IC with original feature set is also tested on different validation (5/10-fold cross-validations) and partitioning (20%–40%) methods. These classifiers are evaluated how to effectively categorize tumors as benign and malignant in terms of specificity, sensitivity, accuracy, F-score, Youden’s index, discriminant power, and the receiver operating characteristic (ROC) curve with its criterion values including area under curve (AUC) and 95% confidential interval (CI). This represents an improvement in diagnostic decision support system, while reducing computational complexity.