Breast cancer has become the most growing cancer, of which the early diagnosis and prediction require precise medical development tools. However, the accuracy of conventional machine learning classification prediction should be improved. Accordingly, ensemble learning has been proposed, a novel idea of machine learning, which is capable of significantly improving the accuracy of prediction and presenting novel insights into breast cancer disk classification prediction. In this paper, six of the latest ensemble learning classification algorithms (i.e., Xgboost, Catboost, GBDT, LGBM, Random Forest and Extra Tree as an ensemble learning model) are compared with one conventional machine learning algorithm (i.e., K Near Neighbor (KNN)). The original breast cancer data set of Wisconsin is adopted to train the model, and the model effect is assessed using model evaluation indicators (e.g., accuracy, recall, and accuracy) after the model is trained. In addition, the Xgboost algorithm is indicated with the maximum prediction accuracy for breast cancer cells. Furthermore, it was revealed that ensemble learning algorithms generally have higher accuracy than other machine learning algorithms.