In today’s world Breastcancer is one of the major problem faced by women . Identifying cancer is theprimitive stage and is stillchallenging.
The diagnosis and treatment of the breast cancer have become anurgent. Breast cancer, is widely seen tumor in Indian women . Early treatment of breast cancer have become anextremely crucial work to do, not onlyhelps to cure cancer but also helps in curative of its occurence. Today , there are different kinds of methods and data mining techniquesand various process like knowledge discovery are developed for predicting the breast cancer. As per the study , we perform a comparison ofdifferent classification and clustering algorithms. Various classification algorithms and theclustering algorithm are used. The result indicate that the classificationalgorithms are better predictors than the clustering algorithm.
Now-a-days breastcancer is common in women. Predicting breast cancer is as important as itstreatment. Breast cancer is the most common cause of death among women.
Ifbreast cancer predicted at its earlier stages, better treatment can be providedwhich enable the person to survive. Diagnosis and treatment of breast cancerhas become an urgent work to perform. Different data mining methods are used toretrieve valuable information from large databases inorder to make decisions toprovide better health services. Breast cancer begins with the abnormal growth of somebreast cells . These cells divide more rapidly and continue to accumulate thanhealthy cells do, forming a lump or mass. These cells may grow through your breast to your lymph nodes or toother parts of your body. Breast cancer varies on the basis of age groups, itis less common at a young age (i.e.
, intheir thirties), younger women lean to have more aggressive breast cancers thanolder women.In this paper we perform comparison on differentclassification as well as clustering algorithm to predict breast cancer. Anumber of attributes are used in performing comparison, theyinclude……………………………. These attributes are compared to find the best classificationalgorithm.