Prediction models for estimation of survival rate and relapse for breast cancer patients

In this paper, we described the practical application of data mining methods for estimation of survival rate and disease relapse for breast cancer patients. A comparative study of prominent machine learning models was carried out and according to the achieved results we concluded that the classifiers obviously learn some of the concepts of breast cancer survivability and recurrence. These algorithms were successfully applied to a novel breast cancer data set of the Clinical Center of Kragujevac. The Naive Bayes classifier is selected as a model for prognosis of cancer survivability on the basis of the 5 years survival rate, while the Artificial Neural Network has achieved the best performance in prognosis of cancer recurrence. Selection of twenty attributes that are the most related to success of prognosis on survivability can give new insights into the set of prognostic factors which need to be observed by medical experts.