A New Feature Selection and Classification Approach for Optimizing Breast Cancer Subtyping Based on Gene Expression

Breast cancer subtypes, which play a significant role in breast cancer prognosis and targeted therapy selection, can be identified with gene expression profiling. It is also beneficial for personalized treatment to know bio-markers that impact the development of cancer cells from studying gene expression. Therefore, this study uses recursive feature elimination, support vector machine classifier with grid search cross-validation to prognosticate breast cancer subtypes and propose the cancer-related bio-markers. We experiment with 2682 samples of gene expression data collected from two different sources. Using the same dataset with the state-of-the-art solution, we achieve an accuracy of 89.40% and improve 5.44% accuracy. Besides, our solution suggests 16 bio-markers associated with cancer that have supporting evidence from the literature and propose 11 new genes potential for future research.