Knowledge Discovery From Medical Data:Extracting Influencing Factors Of Breast Cancer Recurrences Through Predictive Apriori Algorithm

Srinivas Murti, Gururaj R .Patwari, Sriranga Joshi


Breast Cancer is the leading cause of cancer deaths in women .One in nine women is expected to develop breast cancer. Breast cancer can recur at any time, but most recurrences occur in the first three to five years of initial treatment. Breast Cancer can come back as a local recurrence (in the treated Breast or near mastectomy scar) or as a distant recurrence somewhere else in the body. In this work 286 Breast Cancer patient data , obtained from UCI Machine Learning Repository are used to determine the relationship between the Breast cancer recurrences and other attributes through Predictive Apriori algorithm using WEKA(Waikato Environment for Knowledge Analysis) data mining tool

Keywords: Medical data, Data Mining, Association Rules, WEKA, Predictive Apriori Algorithm, Predictive Accuracy, Knowledge Discovery in Databases (KDD)

Full Text:




  • There are currently no refbacks.

Copyright (c) 2016 International Journal of Advanced Research in Computer Science