Breast cancer subtype identification using machine learning techniques

Breast cancer is the most commonly diagnosed cancer and the second leading cause of death among women worldwide. Accurate diagnosis of the specific subtypes of this disease is vital to ensure that patients are provided with the most effective therapeutic strategies that yield the greatest response. Using the newly proposed ten subtypes of breast cancer, we hypothesize that machine learning techniques offer many benefits for selecting the most informative biomarkers. Unlike existing gene selection approaches, in this study, a hierarchical classification approach is used that selects genes and builds the classifier concurrently. Our results support that this modified approach to gene selection yields a small subset of genes that can predict these ten subtypes with greater than 95% overall accuracy.