Exploration of Classification Techniques as a Treatment Decision Support Tool for Patients with Uterine Fibroids

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.