Breast Cancer Detection with Reduced Feature Set.

Ahmet Mert,Erdem Bilgili,Aydin Akan,Niyazi Kılıç

doi:10.1155/2015/265138

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.

Highlights

Breast cancer is one of the leading causes of death among all cancers for women [1]
One-dimensional feature vector of Wisconsin diagnostic breast cancer (WDBC) data reduced using independent component analysis (ICA) is used for training and testing the classifiers
The success of the breast cancer classification is generally evaluated on the basis of sensitivity value because the classifying of the malignant mass is more important than the benign mass

Summary

Introduction

Breast cancer is one of the leading causes of death among all cancers for women [1]. Detection and correct diagnosis of cancer are essential for the treatment of the disease. The traditional approach to cancer diagnosis depends highly on the experience of doctors and their visual inspections. They fail when probabilities have to be assigned to observations [2]. Several tests are applied, exact diagnosis may be difficult even for an expert. That is why automatic diagnosis of breast cancer is investigated by many researchers. Computer aided diagnostic tools are intended to help physicians in order to improve the accuracy of the diagnosis [3,4,5]

Methods

Results

Conclusion