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Cancer is a disease that has become one of the world's concerns because the risk of dying from cancer increases with the increase in population, and not only if someone is infected, but it can also affect a person's individual life. Women's breast cancer is the most common type and the leading cause of death in women worldwide. Early detection of breast cancer can significantly improve survival. If a woman has been identified, then rehabilitation and treatment are needed on an incentive basis to reduce the impact of the worse. This study uses several data mining classification strategies to predict breast cancer. The classification algorithms that we propose are SVM, KNN, Naive Bayes, Random Forest, Decision Tree, Deep Learning, and Neural Network. From these algorithms, we compare accuracy, best grouping, and compare ROC to see which algorithm has the best quality for classification. From the research results, the deep learning algorithm has a good accuracy of 93.49%, the algorithm with the best classification with 90.09%, and not only that; the deep learning algorithm has the best algorithm quality (Compare ROC) on the dataset used in this study

breast cancer, algorithm, classification, accuracy