Considering the commonality of breast cancer among women in the United States and the increasing popularity of precision medicine and data analytics in healthcare, the aim of this study was to use self-organizing maps (SOM) to profile and make decisions about breast cancer patients. Breast cancer mass measurements were combined with nine non-medical attributes—family income, history of cancer, level of education, preference of probability level, presence of dependents, employment status, marital status, age, and location—that were randomly generated based on recent population statistics and fed into a SOM. The SOM’s accuracy was evaluated at around 80%. To show the decision-making capabilities of the SOM, a subset of the patients were treated as new patients and placed on the map. Profiles of these clusters were created to show how decisions made about patients’ diagnosis, delivery, and treatment differed based on the cluster to which they belonged.
| Identifer | oai:union.ndltd.org:MSSTATE/oai:scholarsjunction.msstate.edu:td-1132 |
| Date | 10 August 2018 |
| Creators | Spencer, Vanda Victoria |
| Publisher | Scholars Junction |
| Source Sets | Mississippi State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Theses and Dissertations |
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