Performance Evaluation of Predictive Models for Breast Cancer Classification

Abstract

Breast cancer (BC) is the second most typical carcinoma in women after most of the tumors. With the development of contemporary diagnostic technologies, globalization, and commercialization, higher incidences of this cancer have been documented. BC accounts for 16% of all cancer-related deaths worldwide, making it the most prevalent cause of cancer-related death among women. BC is lethal in just 50% of cases. The purpose of this study is to investigate a report on BC in which the possibility that a patient would survive their illness was predicted using the most recent technological advancements. To create the prediction models utilizing a big dataset, we employed five well-known machine learning (ML) methods LR, DT, NB, KNN and SVM.