Diagnosis of breast cancer tumor based on manifold learning and Support Vector Machine

This paper proposes an efficient algorithm based on manifold learning and support vector machine (SVM) for the diagnosis of breast cancer tumor. First, Isomap algorithm is implemented to project high-dimensional breast tumor data to much lower dimensional space, then the processed data are classified by the SVM. Experimental and analytical results show that in the diagnosis of breast cancer tumor the proposed method can greatly speed up the training and testing of the classifier and get high testing correct rate, superior to the classical principal component analysis (PCA) algorithm.