Breast cancer diagnosis from fluorescence spectroscopy using support vector machine

2007 ◽  
Author(s):  
Jiyoung Choi ◽  
Sharad Gupta ◽  
Inho Park ◽  
Doheon Lee ◽  
Jong Chul Ye
Keyword(s):  
Breast Cancer ◽  
Support Vector Machine ◽  
Fluorescence Spectroscopy ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis ◽  
Support Vector

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

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Na Liu ◽  
Jiang Shen ◽  
Man Xu ◽  
Dan Gan ◽  
Er-Shi Qi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Support Vector Machine ◽  
Cancer Diagnosis ◽  
Classification Accuracy ◽  
Support Vector Machine Classifier ◽  
Treatment Plan ◽  
Breast Cancer Diagnosis ◽  
Support Vector ◽  
Classification Models ◽  
Misclassification Costs

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.

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