Breast-cancer-classification
Support vector machines combined with feature selection for breast cancer diagnosis.
Breast cancer is the second largest cause of cancer deaths among women. At the same time, it is also among the most curable cancer types if it can be diagnosed early. Research efforts have reported with increasing confirmation that the support vector machines (SVM) have greater accurate diagnosis ability. In this project, breast cancer diagnosis based on a SVM-based method combined with feature selection has been proposed.
The performance of the method is evaluated using classification accuracy, sensitivity, specificity, positive and negative predictive values and confusion matrix. The results show that the highest classification accuracy (95.84%) is obtained for the SVM model that contains ten features, and this is very promising compared to the previously reported results.