Breast Cancer Grading using Machine Learning Approach Algorithms

Abstract

Recently, Breast Cancer (BC) becomes a more common cancer disease in women and it considers the most important sign which leads to death among women. Therefore, it requires efficient methods for detecting it to reduce the risk of death. A positive prognosis and greater chances of survival are improved if the BC is detected early. Currently, machine learning plays an important role in diagnosing BC disease. The various techniques in artificial intelligence and machine learning persuade the researchers in exploring their classification systems in classifying and detecting the BC disease. The algorithms are the K-Nearest Neighbor (KNN), the Support Vector Machine (SVM), random forest, logistic regression, and decision tree. In this study, various algorithms of the machine are proposed in designing the classification system for detecting the BC diseases. To improve the resulting quality, the Principal Component Analysis Algorithm (PCA) is applied. The system was tested and evaluated on the Wisconsin BC dataset from the University of Wisconsin Hospitals. The results were interesting and very good. The accuracy, recall, precision, and F-score of the SVM algorithm were obtained by up to 98% compared to previous work.