Early Detection of Breast Cancer Tumors Using Linear Discriminant Analysis Feature Selection with Different Classification Methods

Abstract

Globally, the frequency of breast cancer and its morality speaks to a critical and developing risk for developing countries. In Asia, Pakistan has the biggest rate of breast cancer. It is evaluated that every year 83,000 cases were reported in Pakistan and over 40,000 deaths are caused by breast cancer. Many Early identifications of breast cancer can be achieved using data mining techniques, allowing preventative treatments to be done. Patients suffering from this malignancy have a better chance of surviving if they are diagnosed early. So, there is a need to develop a system to diagnose breast cancer tumors at an early stage. In this research WBCD and Duke Breast cancer datasets (DBDS) are used with Linear Discriminant Analysis (LDA) feature selection. Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF) are classifiers to predict breast cancer tumors.