title: Feature selection using genetic algorithm for breast cancer diagnosis: experiment on three different datasets.
abstract: OBJECTIVE
  This study addresses feature selection for breast cancer diagnosis. The present process uses a wrapper approach using GA-based on feature selection and PS-classifier. The results of experiment show that the proposed model is comparable to the other models on Wisconsin breast cancer datasets.
  
  
  METHODS
  To evaluate effectiveness of proposed feature selection method, we employed three different classifiers artificial neural network (ANN) and PS-classifier and genetic algorithm based classifier (GA-classifier) on Wisconsin breast cancer datasets include Wisconsin breast cancer dataset (WBC), Wisconsin diagnosis breast cancer (WDBC), and Wisconsin prognosis breast cancer (WPBC).
  
  
  RESULTS
  For WBC dataset, it is observed that feature selection improved the accuracy of all classifiers expect of ANN and the best accuracy with feature selection achieved by PS-classifier. For WDBC and WPBC, results show feature selection improved accuracy of all three classifiers and the best accuracy with feature selection achieved by ANN. Also specificity and sensitivity improved after feature selection.
  
  
  CONCLUSIONS
  The results show that feature selection can improve accuracy, specificity and sensitivity of classifiers. Result of this study is comparable with the other studies on Wisconsin breast cancer datasets.
authors.affiliation: Department of Medical Informatics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
authors.forename: Shokoufeh
authors.initials: S
authors.lastname: Aalaei
authors.affiliation: Department of Electrical Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran.
authors.forename: Hadi
authors.initials: H
authors.lastname: Shahraki
authors.affiliation: Robotics Laboratory, Department of Electrical Engineering, University of Neyshabur, Neyshabur, Iran.
authors.forename: Alireza
authors.initials: A
authors.lastname: Rowhanimanesh
authors.affiliation: Pharmaceutical Research Center, School of Pharmacy, Mashhad University of Medical Sciences, Mashhad, Iran; Department of Medical Informatics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran; Department of Medical Informatics, Academic Medical Center, Amsterdam, The Netherlands.
authors.forename: Saeid
authors.initials: S
authors.lastname: Eslami
keywords: Breast cancer; Classification feature; Selection data mining