A Novel Ensemble Method Based on Support Vector Dynamic Learning Neural Network for Breast Cancer Diagnosis

To improve the accuracy of breast cancer diagnosis and reduce examination costs, a novel ensemble learning method called support vector dynamic learning neural network (SVDL) is proposed in this paper. The method first constructs the breast cancer classification problem as a standard quadratic programming (QP) problem based on support vector machine (SVM). Then, a novel dynamic learning neural network (DLNN) solver is designed to solve this problem to obtain the optimal diagnosis model. The experimental results on the Wisconsin Diagnostic Breast Cancer dataset show that the proposed method is superior to traditional and state-of-the-art machine learning methods, achieving the best accuracy (98:59%) and area under curve value (0.9956), as well as high specificity (98:85%) and sensitivity (98:18%). It demonstrates that the proposed method has good classification performance. Furthermore, the proposed method may further enhance the model performance by introducing swarm intelligence algorithm to search for the optimal value of model parameters, which will contribute to the diagnosis of breast cancer and other diseases as well.