Classification of breast cancer using Wrapper and Naïve Bayes algorithms

Abstract

Breast cancer is defined as a malignant neoplasm disease originating from the parenchyma. This disease is most commonly afflicted by women and is the second killer after lung cancer. By the end of 2012, the WHO shows the prevalence of breast cancer worldwide reaching 6.3 million spread across 140 countries. Symptoms of the disease are diagnosed using mammogram. The results will be examined by paramedics, to find out if the cancer is malignant or not. However, the accuracy of the diagnoses obtained is directly proportional to the level of paramedical expertise. The general objective of this research aims to classify breast cancer types based on the extracted breast cancer features, and the specific purpose is to offer a hybrid machine learning method. To help increase the accuracy of the diagnosis, through this study, a CAD-based scheme was developed by applying a combination of Wrapper and Naive Bayes algorithms. The Wrapper algorithm is used at the feature selection stage, while the Naive Bayes algorithm is used at the classification stage. There are 683 data taken from the UCI Knowledge Repository consisting of two class, namely 444 benign cancers and 239 malignant cancers, with 9 types of attributes. The data is divided into two groups, with the various amount for each class in each group. The first group was used as a training group for the feature selection stage, and the second group as the testing group for the classification stage. The classification results show the accuracy up to 99.27%, sensitivity and specificity on each up to 99.30%. Based on these results, the proposed scheme is expected to contribute in the development of CAD for the diagnosis of breast cancer.