Analyzing Breast Cancer Survival Disparities by Race and Demographic Location: A Survival Analysis Approach
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909642584489984 |
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| author | Farha, Ramisa Olukoya, Joshua O. |
| author_facet | Farha, Ramisa Olukoya, Joshua O. |
| contents | This study employs a robust analytical framework to uncover patterns in survival outcomes among breast cancer patients from diverse racial and geographical backgrounds. This research uses the SEER 2021 dataset to analyze breast cancer survival outcomes to identify and comprehend dissimilarities. Our approach integrates exploratory data analysis (EDA), through this we identify key variables that influence survival rates and employ survival analysis techniques, including the Kaplan-Meier estimator and log-rank test and the advanced modeling Cox Proportional Hazards model to determine how survival rates vary across racial groups and countries. Model validation and interpretation are undertaken to ensure the reliability of our findings, which are documented comprehensively to inform policymakers and healthcare professionals. The outcome of this paper is a detailed version of statistical analysis that not just highlights disparities in breast cancer treatment and care but also serves as a foundational tool for developing targeted interventions to address the inequalities effectively. Through this research, our aim is to contribute to the global efforts to improve breast cancer outcomes and reduce treatment disparities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_07191 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Analyzing Breast Cancer Survival Disparities by Race and Demographic Location: A Survival Analysis Approach Farha, Ramisa Olukoya, Joshua O. Machine Learning Applications This study employs a robust analytical framework to uncover patterns in survival outcomes among breast cancer patients from diverse racial and geographical backgrounds. This research uses the SEER 2021 dataset to analyze breast cancer survival outcomes to identify and comprehend dissimilarities. Our approach integrates exploratory data analysis (EDA), through this we identify key variables that influence survival rates and employ survival analysis techniques, including the Kaplan-Meier estimator and log-rank test and the advanced modeling Cox Proportional Hazards model to determine how survival rates vary across racial groups and countries. Model validation and interpretation are undertaken to ensure the reliability of our findings, which are documented comprehensively to inform policymakers and healthcare professionals. The outcome of this paper is a detailed version of statistical analysis that not just highlights disparities in breast cancer treatment and care but also serves as a foundational tool for developing targeted interventions to address the inequalities effectively. Through this research, our aim is to contribute to the global efforts to improve breast cancer outcomes and reduce treatment disparities. |
| title | Analyzing Breast Cancer Survival Disparities by Race and Demographic Location: A Survival Analysis Approach |
| topic | Machine Learning Applications |
| url | https://arxiv.org/abs/2506.07191 |