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.
Campbell, Kevin; Thygeson, Marcus N.; Srivastava, Jaideep; Speedie, Stuart.
Exploration of Classification Techniques as a Treatment Decision Support Tool for Patients with Uterine Fibroids.
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