Abstract
Cancer is a well-known disease that leads to death of human beings and breast cancer (BC) is one of the types of cancer diagnosed in women. About one of eight women is diagnosed with BC during her life time. Treatment for BC can be easy if it is diagnosed early. The approach of this study is to identify a patient having BC or not by different Machine Learning (ML) Techniques. In this study Wisconsin Diagnostic Breast Cancer (WDBC) dataset is going to classify with Support Vector Machine (SVM), k-Nearest Neighbours (k-NN), Naive Bayes (NB), Decision-Tree (DT) and Logistic Regression (LR). There is pre-processing stage prior to classification in which five different classifiers applied with 5-fold cross-validation method. Classification performance is measured by performance measuring parameters i.e.accuracy, sensitivity, and specificity with the use of confusion metrics. The best performance found by SVM with an accuracy of 99.12% after normalization process in this study.
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