Breast Cancer Detection Based on UWB Dataset and Machine Learning

Breast cancer is one of the worst diseases in the world and the most common cancer affected by women. Early detection of cancers allows for faster treatments. Women rarely visit a clinic or hospital for routine tests unless they are ill because of long lines, expensive tests, and life difficulties. Recent studies have focused on early breast cancer diagnosis utilizing non-invasive UWB technologies. This article presents several appropriate supervised machine learning algorithms to detect breast cancer, worked with a user-friendly microwave ultra-wideband (UWB) device within the breast tissue. Two models for compressed breast tissue were created using the CST Microwave Studio simulator. These models generated two patient datasets with differing dielectric properties similar to human tissue. These two datasets are used to train the decision tree (DT), support vector machine (SVM), and nearest neighbor (NN) in order to develop an intelligent classification model that can assist doctors in identifying malignant breast cells. KNN can classify the breast data for the first group with 78% accuracy while the SVM 93% accuracy for the second group.