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Chronic diseases like kidney failure, heart disease, cancer, etc. are the major causes of deaths worldwide. The most dangerous type of disease from which the women of every age group are suffering is 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, a classification algorithm of high accuracy and a lower error rate is desirable. In this research work, the authors have used four 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, the authors have used the Wisconsin Diagnostic Breast Cancer dataset, which contains 569 instances along with 32 attributes and a class attribute, which will predict the type of cancer.

Accuracy Enhancement for Breast Cancer Detection Using Classification and Feature Selection