Exploration of Classification Techniques as a Treatment Decision Support Tool for Patients with Uterine Fibroids
View/Download File
Persistent link to this item
Statistics
View StatisticsJournal Title
Journal ISSN
Volume Title
Title
Exploration of Classification Techniques as a Treatment Decision Support Tool for Patients with Uterine Fibroids
Published Date
2010-04-16
Publisher
Type
Report
Abstract
Uterine fibroids are benign growths in the uterus, for which there are several possible treatment options. Patients and physicians generally approach the decision process based on a combination of the patient's degree of discomfort, patient preferences, and physician practice patterns. In this paper, we examine the use of classification algorithms in combination with meta-learning algorithms as a decision support tool to facilitate more systematic fibroid treatment decisions. A model constructed from both Naive Bayes (with Adaboost) and J48 (with bagging) algorithms gave the best results and could be a useful tool to patients making this decision.
Keywords
Description
Related to
Replaces
License
Series/Report Number
Funding information
Isbn identifier
Doi identifier
Previously Published Citation
Suggested citation
Content distributed via the University Digital Conservancy may be subject to additional license and use restrictions applied by the depositor. By using these files, users agree to the Terms of Use. Materials in the UDC may contain content that is disturbing and/or harmful. For more information, please see our statement on harmful content in digital repositories.