Analysis of Feature Variables for Breast Cancer Diagnosis

  • Jung, Yong Gyu (Department of Medical IT Marketing, Eulji University) ;
  • Kim, Jang Il (Department of Medical IT Marketing, Eulji University) ;
  • Sihn, Sung Chul (Health Care Solutions Division, Fujitsu Korea Co. Ltd.) ;
  • Heo, Jun (Dept. of Information and Communication, Kyungmin University)
  • Received : 2013.09.26
  • Published : 2013.11.30


It is becoming more important as the growing of health information and increasing in cancer patients diagnose over the time gradually. Among the various types of cancer, we focuses on breast cancer diagnosis. The accuracy of breast cancer diagnosis is increasing when the diagnosis is based on evidence and statistics. To do this we use the weka data mining tools and analysis algorithms significantly associated with the decision tree uses rules. In addition, the data pre-processing and cross-validation are used to increase the reliability of the results. The number and cause of the disease becomes important to increase evidence-based medical doctors. As the evidence-based medical, the data obtained from patients in the past through the disease by calculating the probability for future patients to diagnose and predict disease and treatment plan. It can be found by improving the survival rate plays an important role.


  1. Australian Institute of Health and Welfare & National Breast Cancer Centre, Breast cancer is Australia: an overview, 2006. Cancer series No. 34. Cat. no. CAN 29. Canberra:AIHW.
  2. Shmueli Galit, Patal Nitin R. and Bruce Peter C, "Data Mining for Business Intelligence", John Wiley & Sonc Inc., 2006
  3. Yong Gyu Jung, Song Ei Han, Ranking Methods of Web Search using Genetic Algorithm, IWIT, Vol.10 No.3 p91-p96
  4. Hwan Seung Yong, Introduction to Data Mining , Infinitybooks, p223-p241, 2007
  5. Hag Yong Han, Introduction to Pattern Recognition, Hanbit Press, p86 - p88, 2009
  6. Charniak, E. (1991). Bayesian Networks without Tears. AI Magazine, p50-p63.
  7. Yong Gyu Jung, Bum Jun Lee, Features Reduction using Logistic Regression for Spam Filtering, IWIT, Vol.10 No.2 p13-p18