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Breast Cancer Detection with Reduced Feature Set
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 feature...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4452509/ https://www.ncbi.nlm.nih.gov/pubmed/26078774 http://dx.doi.org/10.1155/2015/265138 |
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author | Mert, Ahmet Kılıç, Niyazi Bilgili, Erdem Akan, Aydin |
author_facet | Mert, Ahmet Kılıç, Niyazi Bilgili, Erdem Akan, Aydin |
author_sort | Mert, Ahmet |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-4452509 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-44525092015-06-15 Breast Cancer Detection with Reduced Feature Set Mert, Ahmet Kılıç, Niyazi Bilgili, Erdem Akan, Aydin Comput Math Methods Med Research Article 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. Hindawi Publishing Corporation 2015 2015-05-19 /pmc/articles/PMC4452509/ /pubmed/26078774 http://dx.doi.org/10.1155/2015/265138 Text en Copyright © 2015 Ahmet Mert et al. https://creativecommons.org/licenses/by/3.0/ 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. |
spellingShingle | Research Article Mert, Ahmet Kılıç, Niyazi Bilgili, Erdem Akan, Aydin Breast Cancer Detection with Reduced Feature Set |
title | Breast Cancer Detection with Reduced Feature Set |
title_full | Breast Cancer Detection with Reduced Feature Set |
title_fullStr | Breast Cancer Detection with Reduced Feature Set |
title_full_unstemmed | Breast Cancer Detection with Reduced Feature Set |
title_short | Breast Cancer Detection with Reduced Feature Set |
title_sort | breast cancer detection with reduced feature set |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4452509/ https://www.ncbi.nlm.nih.gov/pubmed/26078774 http://dx.doi.org/10.1155/2015/265138 |
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