Breast Cancer Prediction: The Classification of Non-Recurrence-Events and Recurrence-Events Using Functions Classifiers

Each year, the percentage of breast cancer related deaths increases dramatically. It represents most frequent kind of cancer and it is the main reason why women worldwide pass away. Any improvement in the prediction and diagnosis of cancer is essential for living a healthy life. Consequently, a high level of accuracy in cancer prognosis is essential for improving the treatment aspect and the standard of patient survival. Machine learning's approaches can help a lot with predicting and diagnosing breast cancer early. They have become a popular area for researches and have been shown to be a useful method. Almost researches are studies for prediction and diagnosis, the most important thing after the patient survive is to ensure the breast cancer does not recurrence in the future. Rarely studies is talking about this issue.This study discussing the recurrence and non-recurrence breast cancer events after the patient survive, to analyze and classify this issue we used data about breast cancer And depend on the classification classifiers (functions classifier) of machine learning:(Logistic classifier, SGD classifier, SGDText classifier, SimpleLogistic classifier, SMO classifier, VotedPreceptron classifier),to find the most effective classifier for the best result of prediction the recurrence and non-recurrence breast cancer events. The high-level classifier is SGDText and the low-level classifier is the logistic according to the results of this paper.