• Prof.Bhanudas Suresh Panchbhai

analyzing massive amounts of data is becoming a requirement. People do not have time to examine extraordinarily huge data sets such as medical, marketing, or financial data. As a result, we'll need a method for automatically analyzing data. The process of extracting valuable information from vast amounts of data and analyzing, classifying, and summarizing it into useful information from big data is known as data mining. For the diagnosis of breast cancer, a data mining classification technique is applied. In this study, we compare the performance of several classifiers on the basis of accuracy, recall, precision, F-measure, computing time, correctly classified instances, and kappa statistics, MAE, RMSE, RAE, RRSE on a breast cancer dataset. To easily assess the classifiers, we include confusion matrices from various classifiers. We looked at a variety of data mining classification methods in order to find the best ones for efficiently classifying the Breast Cancer dataset.

Data Mining, WEKA tool, Breast Cancer Patients dataset, Decision Tree Classification algorithm

"Analysis of Decision Tree Classification algorithms for Breast Cancer detection", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.1448-1453, June-2019, Available :http://www.jetir.org/papers/JETIR1908D84.pdf

"Analysis of Decision Tree Classification algorithms for Breast Cancer detection", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp1448-1453, June-2019, Available at : http://www.jetir.org/papers/JETIR1908D84.pdf