Purpose/Significance To predict the disease risk of potential or existing breast tumor patients by using a decision tree classification model to simulate the expert consultation idea.Method/Process A C4.5 classical classification algorithm and a pessimistic pruning method are used to predict the outcome of patient pre-consultation for case data collected from the study.Result/Conclusion A C4.5 decision tree with 76 leaf nodes and “whether postoperative radiotherapy or chemotherapy ends in the hospital” as the root node is generated with 95% prediction accuracy and classified into 3 risk levels according to the classification labels.