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Breast cancer (BC) is the most common cancer in women, affecting about 10% of all women at some stages of their life. In recent years, the incidence rate keeps increasing and data show that the survival rate is 88% after five years from diagnosis and 80% after 10 years from diagnosis. Early prediction of breast cancer is one of the most crucial works in the follow-up process. Data mining methods can help to reduce the number of false positive and false negative decisions. Consequently, new methods such as knowledge discovery in databases (KDD) has become a popular research tool for medical researchers who try to identify and exploit patterns and relationships among large number of variables, and predict the outcome of a disease using historical cases stored in datasets. In this paper, using data mining techniques, authors developed models to predict the recurrence of breast cancer by analyzing data collected from ICBC registry. The next sections of this paper review related work, describe background of this study, evaluate three classification models (C4.5 DT, SVM, and ANN), explain the methodology used to conduct the prediction, present experimental results, and the last part of the paper is the conclusion. To estimate validation of the models, accuracy, sensitivity, and specificity were used as criteria, and were compared. (Using Three Machine Learning Techniques for Predicting Breast Cancer Recurrence Ahmad LG)
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Last date updated on July, 2014

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