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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Series: | Mathematical Problems in Engineering |
Online Access: | http://dx.doi.org/10.1155/2018/3875082 |
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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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