Authors: Erick Gutierrez, Sazib Hasan, Vinodh Chellamuthu, Jie Liu
Mentors: Sazib Hasan
Insitution: Utah Tech University
Breast Cancer is the second most common cancer among women in the United States.In 2023, the American Cancer Society anticipates the diagnosis of 297,790 new cases of invasive breast cancer, with approximately 43,700 women expected to lose their lives to this disease. It is crucial to undertake research endeavors aimed at discerning genetic sequence patterns to facilitate the classification and treatment of breast cancer. Recent work has shown that Machine Learning techniques are effective at classifying breast cancer using genetic sequences. Our research employs the METABRIC Breast Cancer Gene Expression Profiles dataset and machine learning techniques like Multi-Layer Perceptrons (MLP) and Random Forest to explore the link between survivability, treatment, and specific genes in breast cancer patients. Predicting survival based on gene sequences and treatments can inform effective countermeasures and research priorities.