Full metadata
Title
Naïve Bayes Classification for Analyzing Prostate Cancer Treatment Outcomes
Description
Prostate cancer is the second most common kind of cancer in men. Fortunately, it has a 99% survival rate. To achieve such a survival rate, a variety of aggressive therapies are used to treat prostate cancers that are caught early. Androgen deprivation therapy (ADT) is a therapy that is given in cycles to patients. This study attempted to analyze what factors in a group of 79 patients caused them to stick with or discontinue the treatment. This was done using naïve Bayes classification, a machine-learning algorithm. The usage of this algorithm identified high testosterone as an indicator of a patient persevering with the treatment, but failed to produce statistically significant high rates of prediction.
Date Created
2016-12
Contributors
- Millea, Timothy Michael (Author)
- Kostelich, Eric (Thesis director)
- Kuang, Yang (Committee member)
- Computer Science and Engineering Program (Contributor)
- Barrett, The Honors College (Contributor)
Topical Subject
Resource Type
Extent
23 pages
Language
eng
Copyright Statement
In Copyright
Primary Member of
Series
Academic Year 2016-2017
Handle
https://hdl.handle.net/2286/R.I.42552
Level of coding
minimal
Cataloging Standards
System Created
- 2017-10-30 02:50:58
System Modified
- 2021-08-11 04:09:57
- 3 years 3 months ago
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