New methods like knowledge discovery in databases (KDD) has become a well-liked research tool for medical researchers who attempt to identify and exploit patterns and relationships among sizable amount of variables, and predict the result of a disease using historical cases stored in datasets. During this paper, using data processing techniques, authors developed models to predict the recurrence of carcinoma by analyzing data collected from ICBC registry. subsequent 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 wont to conduct the prediction, present experimental results, and therefore the last a part of the paper is that 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 carcinoma Recurrence Ahmad LG).
