Early Detection of Breast Cancer Tumors using Linear Discriminant Analysis Feature Selection with Different Machine Learning Classification Methods

Abstract

Globally, the frequency of breast cancer and its morality speak to a critical and developing risk for the 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. Patients suffering from this malignancy have a better chance of surviving if they are diagnosed early. Many Early identification of breast cancer can be achieved using data mining techniques, allowing preventative treatments to be done. In this research Wisconsin Breast Cancer Dataset (WBCD) and Duke Breast cancer dataset (DBDS) are used with Linear Discriminant Analysis (LDA) feature selection with Support Vector Machine (SVM), Decision Tree (DT), Neural Network and Random Forest (RF) machine learning classifiers to predict breast cancer tumors. The finding of the proposed model is that feature selections through LDA improve the accuracy of detecting tumors and also reduce time duration of executing model. The best machine learning model with LDA feature selection is Neural Network Model with highest accuracy 1.00 among all classification models and also consume less time.