Ensemble-Based Survival Analysis of Breast Cancer Recurrences Using Advanced Sensing and SEER Data
Main Article Content
Abstract
Breast cancer recurrence is a challenging issue that profoundly impacts patient well-being, healthcare systems, and society. Modern sensing technologies provide an unprecedented opportunity to gain valuable insights and uncover patterns related to recurrent events. Despite this potential, few studies have delved into survival analysis of breast cancer recurrences based on the extensive healthcare data available. Leveraging this data is crucial for understanding the factors contributing to breast cancer recurrence. This paper introduces an ensemble method known as random survival forest for analyzing time-to-event patterns of breast cancer recurrences using the Surveillance, Epidemiology, and End Results (SEER) data. Our model characterizes the survival probabilities among patients with and without breast cancer recurrences. Ensemble models are constructed by systematically sampling and bootstrapping big data sources. Our experimental findings reveal that the age at the time of cancer recurrence and the time between recurrences follow approximately Gaussian and exponential distributions with means of 61.35 ± 14.03 and 2.61 years, respectively. Moreover, the results identify significant factors such as age, surgical status, tumor stage, and histological grade that influence the likelihood of breast cancer recurrences. This proposed approach in survival analysis holds great potential for aiding healthcare practitioners in the prognosis, treatment, and decision-making regarding breast cancer recurrences.
Downloads
Metrics
Article Details
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
