Abstract: The purpose of this study is are view of the automatic methods of cancer detection in terms of accuracy, speed, error and the number of properties and we have selected the breast cancer as the subject of the case study. The data used in this academic study area courtesy of the UCI in California. This database is called the wisconsin breast cancer datasets and includes 699 data units divided into benign and malignant classes. Ten properties wereassigned to each datum. Four types of algorithms are used in this study, namely, classification algorithms, vector machine algorithms, neural networks algorithms and data mining algorithms. Each category was evaluated separately and the best method in each category was identified in terms of accuracy, speed, error and the number of properties.