Abstract
Breast cancer is one of the most prevalent types of cancer among women. With increased emphasis towards cancer related research, many data-driven research works have been conducted for classifying cancer diagnosis, survival, or recurrence. Unlike existing literature, this study aims to discover interesting subgroup patterns of long-term and short-term survival from the breast cancer incidence data of the SEER (Surveillance, Epidemiology, and End Results) Program. We present a rule induction method for subgroup discovery, which can effectively find subgroup patterns by focusing on local exceptionality detection in contrast to global models. The significance of subgroup patterns discovered is examined with statistical tests. Furthermore the characteristics of two exceptional high and low survival groups are compared by examining the descriptive statistics of prognostic factors in each group. The case study’s results show that the proposed subgroup mining and statistical test approach is a promising technique for clinical and medical data analytics.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
