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According to World HealthOrganization (WHO), breast cancer is the top cancer in women both in the developed and the developing world. Increased life expectancy, urbanization and adoption of western lifestyles trigger the occurrence of breast cancer in the developing world. Most ca ncer events are diagnosed in the late phases of the illness and so, early detection in order to improve breast cancer outcome and survival is very crucial. In this study, it is intended to contribute to the early diagnosis of breast cancer. An analysis onbreast cancer diagnoses for the patients is given. For the purpose, first of all, data about the patients whose cancers’ have already been diagnosed is gathered and they are arranged, and then whether the other patients are in trouble with breast cancer ito be predicted under cover of those data. Predictions of the other patients are realized through seven different algorithms and the accuracies of those have been given. The data about the patients have been taken from UCI Machine Learning Reposito ry thanks to Dr. William H. Wolberg from the University of Wisconsin Hospitals, Madison. During the prediction process, RapidMiner 5.0 data mining tool is used to apply data mining with the desired algorithms.

Breast Cancer Diagnosis Via Data Mining: Performance Analysis of Seven Different Algorithms