Developing an Effective Method to detect Breast Cancer using a Combination of Techniques in Data Mining, SVM and Reduction Features

Breast cancer is the most common gynecological disease that damages personal, family and social widely used. Recognizing the signs and take necessary measures for early detection and timely treatment can have a role in preventing the complications of this disease, Although each member of the human body may be cancer, but breast cancer more than other types of cancer cause of death of women s involvement. As technology advances, new features and characteristics collected for the disease, which increases the number and size of the medical database. Therefore, using data mining techniques for processing these features, it is integral. Different people from different data mining techniques are used to diagnose this disease, But still have not reached the desired result, in most of these methods focus on the implementation of various algorithms on a set of data and compare the performance of these algorithms have been together, and to incorporate algorithms have been studied less. In this study is to select and combine two features useful in diagnosis algorithm SVM, K-Means, which so far have not been combined, Raise the detection accuracy than previous work and benign and malignant breast tumors to predict in the early stages of disease.

Developing an Effective Method to detect Breast Cancer using a Combination of Techniques in Data Mining, SVM and Reduction Features Keywords:

Detecting Breast Cancer , Data Mining Algorithms , Reduction Features , Support Vector Machine