Performance Comparison of Classification Algorithms for Diagnosis of Breast Cancer
Parimala.S, Dr.Senthil Vadivu
Breast cancer, Classification algorithms, UCI repository, data mining
Breast cancer is the one of the maximum analyzed cancer amongst women everywhere in the world. The growth and the expansion diagnosis tools is indispensable assist the pathologists to correctly infer and classify between malignant and benign tumors. Breast Cancer which took away 6, 25,000 lives alone. Because of the less attention of the breast cancer by not detecting the early stage and if it is done preventive measures can be taken to reduce the death rate. There are massive amount of technologies and methods are available in data mining area in predicting Brest cancer. This simulation paper work emphases on dissimilar classification techniques and implementation for data mining in prophesying malignant and benign breast cancer. The experimental dataset is taken from the Breast Cancer Wisconsin data set UCI repository while parameter clump thickness being used as assessment class.