Decision Support Tool for Uterine Fibroids Treatment with Machine Learning Algorithms – A Study

Abstract

- Uterine fibroids are benign growth in the tissues of the uterus which gives discomforts in the form of symptoms like over bleeding, pain in lower abdomen, irregular periods, misconception, in conception etc., 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. While there have been many successes in applying data mining technology to the improvement of diagnostic accuracy. In this paper the use of classification algorithms in combination with Machine learning algorithms as a decision support tool to facilitate more systematic fibroid treatment decisions is examined. Machine learning algorithms like Decision Tree Classifier, Gaussian Naive Bayes, Random Forest Classifier, K-Nearest Neighbours, Gradient Boosting Classifier and XG Boost Classifier algorithms results are used to decide the possible decision for treatment.