A new classifier for breast cancer detection based on Naïve Bayesian

Breast cancer is known as the most common invasive cancer type among women and automatic breast cancer detection systems are in demand. Thus, various machine learning and pattern recognition techniques have been proposed to detect breast cancer. One of these techniques is the Bayes classifier. Naïve Bayesian (NB) is known to be a simple classifier, which is based on the Bayes theorem. There have been so many applications used in literature. In this paper, a new NB (weighted NB) classifier was proposed and its application on breast cancer detection was presented. Several experiments were conducted to evaluate the performance of the weighted NB on the breast cancer database. The experiments were realized with 5-fold cross validation test. Moreover, various performance evaluation techniques namely sensitivity, specificity and accuracy are considered. According to the experiments, the weighted NB obtained the following evaluation values. The calculated sensitivity, specificity and the accuracy values are 99.11%, 98.25%, and 98.54% respectively. Moreover, a comparison with the existing methods in the literature was presented. As a result, the performance of weighted NB is better than regular NB and many other existing methods.