Breast cancer is a disease that generates more than 200,00 new cases each year. This is also a disease that 1/8 of women would end up developing in their lifetime. This is also a disease that affects women of different demographics in different ways. For example, Black women are more likely to die from breast cancer than any other racial group while also being the leading cause of death for them. It is also the leading cause of death for Latina women. With that, what factors contribute to disparities in breast cancer in the United States?
This project aims to explore the health disparities among women who experience breast cancer. This project will be using a dataset provided by the SEER program of the NCI with a Kaggle dataset taken from a section of the program. Using machine learning & data analysis, conclusions will be drawn about the relationship between socioeconomic factors (demographic, race, age, etc.) and a woman’s symptoms and treatment. Questions about if a relationship exists, if one factor impacts another, and more will be explored.
Does race, age, and income impact the survival months of a person?
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What is the relationship between income & breast cancer survival months? Is there one?
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What is the relationship between race & breast cancer survival months? Is there one?
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What is the relationship between age & breast cancer survival months? Is there one?
How do the breast cancer stages (T, N, M) affect survival months?
Does demographic determine a person's breast cancer stage?