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 yourbreast to your lymph nodes or to other parts of your body. Breast cancer varies on the basis of age groups, itis less common at a young age (i.e., in their thirties), youngerwomen lean to have more aggressive breast cancers than older women.In this paper we perform comparison on different classificationas well as clustering algorithm to predict breast cancer. A number ofattributes are used in performing comparison, they include……………………………. Theseattributes are compared to find the best classification algorithm. Literature survey In paper 1, threedifferent data mining classification methods are used for the prediction ofbreast cancer.
it compared on different parameters for prediction of cancer.But for superior prediction, focus is onaccuracy and lowest computing time. studies filtered all algorithms based onlowest computing time and accuracy and we came up with the conclusion thatNaïve Bayes is a superior algorithm compared to the two others because it takeslowest time i.
e. 0.02 seconds and at the same time is providing highestaccuracy. In future we will compare results with other supervised as well asunsupervised methods and compare their performances. Reference1 Chintan Shah; Anjali G. Jivani “Comparison of datamining classification algorithms for breast cancer prediction”2 Uma Ojha; Savita Goel “A study on prediction of breast cancer recurrence using data mining techniques” 2017 7th International Conference on Cloud Computing, DataScience & Engineering – Confluence3 Runjie Shen Yuanyuan Yang Fengfeng Shao “Intelligent BreastCancer Prediction Model Using Data Mining Techniques”4Ahmed Iqbal Pritom; Md.
Ahadur Rahman Munshi; Shahed Anzarus Sabab;Shihabuzzaman Shihab.”Predicting breast cancer recurrence using effective classification and feature selection technique”5 S.Padmapriya, M.Devika,V.Meena, S.B.Dheebikaa &R.Vinodhini , ” Survey on Breast CancerDetection Using Weka Tool”6 Jahanvi Joshi, Rinal Doshi, Jigar Patel, Ph.D,” Diagnosis of Breast Cancer using Clustering Data Mining Approach”