title: Applying Data Mining Techniques to Extract Hidden Patterns about Breast Cancer Survival in an Iranian Cohort Study.
abstract: BACKGROUND
  Breast cancer survival has been analyzed by many standard data mining algorithms. A group of these algorithms belonged to the decision tree category. Ability of the decision tree algorithms in terms of visualizing and formulating of hidden patterns among study variables were main reasons to apply an algorithm from the decision tree category in the current study that has not studied already.
  
  
  METHODS
  The classification and regression trees (CART) was applied to a breast cancer database contained information on 569 patients in 2007-2010. The measurement of Gini impurity used for categorical target variables was utilized. The classification error that is a function of tree size was measured by 10-fold cross-validation experiments. The performance of created model was evaluated by the criteria as accuracy, sensitivity and specificity.
  
  
  RESULTS
  The CART model produced a decision tree with 17 nodes, 9 of which were associated with a set of rules. The rules were meaningful clinically. They showed in the if-then format that Stage was the most important variable for predicting breast cancer survival. The scores of accuracy, sensitivity and specificity were: 80.3%, 93.5% and 53%, respectively.
  
  
  CONCLUSIONS
  The current study model as the first one created by the CART was able to extract useful hidden rules from a relatively small size dataset.
authors.affiliation: Inpatient's Safety Research Center, Department of Biostatistics, School of Medicine, Urmia University of Medical Sciences, Urmia, Iran.
authors.forename: Hamid Reza
authors.initials: HR
authors.lastname: Khalkhali
authors.affiliation: Department of Health Information Technology, School of Paramedicine, Urmia University of Medical Sciences, Urmia, Iran.
authors.forename: Hadi
authors.initials: H
authors.lastname: Lotfnezhad Afshar
authors.affiliation: Omid Treatment and Research Center, Urmia, Iran.
authors.forename: Omid
authors.initials: O
authors.lastname: Esnaashari
authors.affiliation: Solid Tumor Research Center, Department of Medical Physics and Imaging, School of Paramedicine, Urmia University of Medical Sciences, Urmia, Iran. njabbarimp@gmail.com.
authors.forename: Nasrollah
authors.initials: N
authors.lastname: Jabbari
mesh_terms: D000328:Adult; D000465:Algorithms; D001943:Breast Neoplasms / Q000401:mortality*; D015331:Cohort Studies; D057225:Data Mining / Q000379:methods*; D003663:Decision Trees; D005260:Female; D006801:Humans; D007492:Iran / Q000453:epidemiology; D008875:Middle Aged; D015233:Models, Statistical; D012680:Sensitivity and Specificity; D016019:Survival Analysis
keywords: Breast Neoplasms; CART; Data Mining; Decision Tree; Survival