R-Based Machine Learning for Uterus Fibroid Detection and Prediction

Abstract

The growth of benign tumors in the uterus is known as uterine fibroids. By the age of 50, they have affected as many as 70% of women, making them the most frequent gynecological condition. Although the majority of fibroids do not create any noticeable symptoms, a small number of them might lead to issues like heavy periods, pelvic pain, and infertility. For better patient outcomes, uterine fibroids should be detected and diagnosed early. Uterine fibroids can be accurately detected and predicted with the use of machine learning methods. Here, we provide an innovative approach to uterine fibroids detection and prediction in R-based Support Vector Machine (SVM). We test our approach against other machine learning methods using a dataset of patients with uterine fibroids.