Improved Cost-Sensitive Support Vector Machine Classifier for Breast Cancer Diagnosis

As one of the most prevalent cancers among women worldwide, breast cancer has attracted the most attention by researchers. It has been verified that an accurate and early detection of breast cancer can increase the chances for the patients to take the right treatment plan and survive for a long time...

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Main Authors: Na Liu, Jiang Shen, Man Xu, Dan Gan, Er-Shi Qi, Bo Gao
Format: Article
Language:English
Published: Hindawi Limited 2018-01-01
Series:Mathematical Problems in Engineering
Online Access:http://dx.doi.org/10.1155/2018/3875082
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spelling doaj-df820503455e4720bd2874c086515b132020-11-25T02:34:20ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472018-01-01201810.1155/2018/38750823875082Improved Cost-Sensitive Support Vector Machine Classifier for Breast Cancer DiagnosisNa Liu0Jiang Shen1Man Xu2Dan Gan3Er-Shi Qi4Bo Gao5College of Management and Economics, Tianjin University, Tianjin 300072, ChinaCollege of Management and Economics, Tianjin University, Tianjin 300072, ChinaBusiness School, Nankai University, Tianjin 300071, ChinaCollege of Management and Economics, Tianjin University, Tianjin 300072, ChinaCollege of Management and Economics, Tianjin University, Tianjin 300072, ChinaSchool of Computer Science and Technology, Anhui University, Hefei 230601, ChinaAs one of the most prevalent cancers among women worldwide, breast cancer has attracted the most attention by researchers. It has been verified that an accurate and early detection of breast cancer can increase the chances for the patients to take the right treatment plan and survive for a long time. Nowadays, numerous classification methods have been utilized for breast cancer diagnosis. However, most of these classification models have concentrated on maximum the classification accuracy, failed to take into account the unequal misclassification costs for the breast cancer diagnosis. To the best of our knowledge, misclassifying the cancerous patient as non-cancerous has much higher cost compared to misclassifying the non-cancerous as cancerous. Consequently, in order to tackle this deficiency and further improve the classification accuracy of the breast cancer diagnosis, we propose an improved cost-sensitive support vector machine classifier (ICS-SVM) for the diagnosis of breast cancer. In the proposed approach, we take full account of unequal misclassification costs of breast cancer intelligent diagnosis and provide more reasonable results over previous works and conventional classification models. To evaluate the performance of the proposed approach, Wisconsin Breast Cancer (WBC) and Wisconsin Diagnostic Breast Cancer (WDBC) breast cancer datasets obtained from the University of California at Irvine (UCI) machine learning repository have been studied. The experimental results demonstrate that the proposed hybrid algorithm outperforms all the existing methods. Promisingly, the proposed method can be regarded as a useful clinical tool for breast cancer diagnosis and could also be applied to other illness diagnosis.http://dx.doi.org/10.1155/2018/3875082
collection DOAJ
language English
format Article
sources DOAJ
author Na Liu
Jiang Shen
Man Xu
Dan Gan
Er-Shi Qi
Bo Gao
spellingShingle Na Liu
Jiang Shen
Man Xu
Dan Gan
Er-Shi Qi
Bo Gao
Improved Cost-Sensitive Support Vector Machine Classifier for Breast Cancer Diagnosis
Mathematical Problems in Engineering
author_facet Na Liu
Jiang Shen
Man Xu
Dan Gan
Er-Shi Qi
Bo Gao
author_sort Na Liu
title Improved Cost-Sensitive Support Vector Machine Classifier for Breast Cancer Diagnosis
title_short Improved Cost-Sensitive Support Vector Machine Classifier for Breast Cancer Diagnosis
title_full Improved Cost-Sensitive Support Vector Machine Classifier for Breast Cancer Diagnosis
title_fullStr Improved Cost-Sensitive Support Vector Machine Classifier for Breast Cancer Diagnosis
title_full_unstemmed Improved Cost-Sensitive Support Vector Machine Classifier for Breast Cancer Diagnosis
title_sort improved cost-sensitive support vector machine classifier for breast cancer diagnosis
publisher Hindawi Limited
series Mathematical Problems in Engineering
issn 1024-123X
1563-5147
publishDate 2018-01-01
description As one of the most prevalent cancers among women worldwide, breast cancer has attracted the most attention by researchers. It has been verified that an accurate and early detection of breast cancer can increase the chances for the patients to take the right treatment plan and survive for a long time. Nowadays, numerous classification methods have been utilized for breast cancer diagnosis. However, most of these classification models have concentrated on maximum the classification accuracy, failed to take into account the unequal misclassification costs for the breast cancer diagnosis. To the best of our knowledge, misclassifying the cancerous patient as non-cancerous has much higher cost compared to misclassifying the non-cancerous as cancerous. Consequently, in order to tackle this deficiency and further improve the classification accuracy of the breast cancer diagnosis, we propose an improved cost-sensitive support vector machine classifier (ICS-SVM) for the diagnosis of breast cancer. In the proposed approach, we take full account of unequal misclassification costs of breast cancer intelligent diagnosis and provide more reasonable results over previous works and conventional classification models. To evaluate the performance of the proposed approach, Wisconsin Breast Cancer (WBC) and Wisconsin Diagnostic Breast Cancer (WDBC) breast cancer datasets obtained from the University of California at Irvine (UCI) machine learning repository have been studied. The experimental results demonstrate that the proposed hybrid algorithm outperforms all the existing methods. Promisingly, the proposed method can be regarded as a useful clinical tool for breast cancer diagnosis and could also be applied to other illness diagnosis.
url http://dx.doi.org/10.1155/2018/3875082
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