Breast Cancer Classification Using Machine Learning

Abstract

Breast cancer is especially dangerous to women, with a high rate of morbidity and mortality. Therefore, the need for algorithm that detects the early sign of breast cancer is essential. The four classification algorithms logistic regression, decision tress, random forest and CNN predicts the breast cancer and then outcomes were compared. Machine learning plays important role in prediction of early sign of breast cancer. In this project we will be using three different classification machine learning algorithms. Then we will compare the accuracy and performance of all these algorithms. Data with imbalanced classes are a big problem in classification algorithms and hence requires proper preprocessing and handling. We will use dataset of breast cancer patients and then train the different machine learning models. In the end all the algorithms are compared based of performance and accuracy to find out which algorithm is the best for this problem. This study will show the accuracy of several models for this breast cancer categorization so that the appropriate approach may be employed. The purpose of this study is to forecast the accuracy of several breast cancer categorization algorithms.