cancer is common in women. Predicting breast cancer is as important as its
treatment. Breast cancer is the most common cause of death among women. If
breast cancer predicted at its earlier stages, better treatment can be provided
which enable the person to survive. Diagnosis and treatment of breast cancer
has become an urgent work to perform. Different data mining methods are used to
retrieve valuable information from large databases inorder to make decisions to
provide better health services.
Breast cancer begins with the abnormal growth of some
breast cells . These cells divide more rapidly and continue to accumulate than
healthy cells do, forming a lump or mass. These cells may grow through your
breast to your lymph nodes or to other parts of your body. Breast cancer varies on the basis of age groups, it
is less common at a young age (i.e., in their thirties), younger
women lean to have more aggressive breast cancers than older women.
In this paper we perform comparison on different classification
as well as clustering algorithm to predict breast cancer. A number of
attributes are used in performing comparison, they include……………………………. These
attributes are compared to find the best classification algorithm.
In paper 1, three
different data mining classification methods are used for the prediction of
breast cancer. it compared on different parameters for prediction of cancer.
But for superior prediction, focus is on
accuracy and lowest computing time. studies filtered all algorithms based on
lowest computing time and accuracy and we came up with the conclusion that
Naïve Bayes is a superior algorithm compared to the two others because it takes
lowest time i.e. 0.02 seconds and at the same time is providing highest
accuracy. In future we will compare results with other supervised as well as
unsupervised methods and compare their performances.
1 Chintan Shah; Anjali G. Jivani “Comparison of data
mining 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, Data
Science & Engineering – Confluence
3 Runjie Shen Yuanyuan Yang Fengfeng Shao “Intelligent Breast
Cancer Prediction Model Using Data Mining Techniques”4
Ahmed 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 Cancer
Detection Using Weka Tool”
6 Jahanvi Joshi, Rinal Doshi, Jigar Patel, Ph.D,” Diagnosis of Breast Cancer using Clustering Data Mining Approach”