Prognosis prediction of human breast cancer by integrating deep neural network and support vector machine: Supervised feature extraction and classification for breast cancer prognosis prediction

Abstract

Breast cancer is the second most common cancer, which accounts for about 16% of all female cancers and 18.2% of all cancer deaths worldwide including both males and females. Therefore, accurate prognosis prediction of breast cancer is one of the most crucial works in follow-up treatment. In this work, we introduced a novel deep learning (DL) method named D-SVM for the prediction of human breast cancer prognosis. D-SVM was a supervised feature extraction and classification method integrating deep neural network and traditional support vector machine (SVM) classifier. This novel algorithm effectively learned hierarchical and abstract representation from raw input data and successfully integrated traditional classification method. Our experimental results clearly indicated that the proposed method could enable us to train a model that yielded reasonably good performance than traditional classification approaches on a small-scale gene expression dataset for breast cancer.