Survey on mining techniques for breast cancer related data

In the paper we are showing a comparative study of some of the classification and the clustering algorithms so that we can find the alternatives for the datasets depending upon the requirement. These prediction systems can be used for the medical diagnosis as various algorithms in this paper have been categorized and simulation results shown. Prevalence informs the total case load at a given time. Incidence yields a pointer to extent of attention required and choice of measures. Different classification algorithms are trained on the result set to build the final classifier model based on K-fold cross validation method. The best accuracy for the given dataset is achieved in rotation forest algorithm compared to other classifiers. The proposed approach will help sort out the. This methodology is evaluated using 286 raw breast cancer data obtained from a city hospital. The best accuracy for the given dataset is achieved in bagging algorithm compared to other classifiers. The proposed approach helps doctors in their diagnosis decisions and also in their treatment planning procedures for different categories.