Accuracy Enhancement for Breast Cancer Detection Using Classification and Feature Selection

Abstract

Chronic disease like kidney failure, heart disease, cancer etc. is the major cause of deaths now days worldwide. Especially for the females the most dangerous type of disease from which the women of every age group are suffering especially the middle age group women’s is the breast cancer. To detect this type of disease at an early stage is a challenging task. In order to predict the breast cancer at an early stage classification algorithm of high accuracy and less error rate are desirable. In this research work we have used 4 classification algorithms K-NN, J48, Logistic regression and Bayes Net for building the predictive model, also the wrapper method of feature selection is used to enhance the accuracy rate and reduce the error rate of the used classifiers. To carry out this research we have used Wisconsin Diagnostic Breast Cancer dataset which contains 569 instances along with 32 attributes and a class attribute which will predict the type of cancer i.e. Benign or Malignant.