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Analysis of Feature Variables for Breast Cancer Diagnosis

  • Jung, Yong Gyu ;
  • Kim, Jang Il ;
  • Sihn, Sung Chul ;
  • Heo, Jun
  • Received : 2013.09.26
  • Published : 2013.11.30

Abstract

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.

Keywords

diagnostic criteria;Breast-cancer;Cervical Cancer;10-fold Validation

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