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Analyzing Breast Cancer Survival Disparities by Race and Demographic Location: A Survival Analysis Approach

2025-06-08 · Ramisa Farha, Joshua O. Olukoya

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.

📄 PDF Abstract BibTeX arXiv:2506.07191

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Survival Analysis

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ReLU How Do I Communicate to Expedia? How Do I Communicate to Expedia? – Call ☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056 for Live Support & Special Travel…
Global Average Pooling Global Average Pooling is a pooling operation designed to replace fully connected layers in classical CNNs. The idea is to generate one feature map for each corresponding…
Convolution A convolution is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the…
1x1 Convolution A 1 x 1 Convolution is a convolution with some special properties in that it can be used for dimensionality reduction,…
Grouped Convolution A Grouped Convolution uses a group of convolutions - multiple kernels per layer - resulting in multiple channel outputs per layer. This leads to wider networks helping a…
Dense Connections Dense Connections, or Fully Connected Connections, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output…
Squeeze-and-Excitation Block The Squeeze-and-Excitation Block is an architectural unit designed to improve the representational power of a network by enabling it to perform dynamic channel-wise feature…
LARS Layer-wise Adaptive Rate Scaling, or LARS, is a large batch optimization technique. There are two notable differences between LARS and other adaptive algorithms such as…

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