Breast cancer diagnosis using Genetically Optimized Neural Network model

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Bhardwaj:2015:ESA,
  author =       "Arpit Bhardwaj and Aruna Tiwari",
  title =        "Breast cancer diagnosis using Genetically Optimized
                 Neural Network model",
  journal =      "Expert Systems with Applications",
  volume =       "42",
  number =       "10",
  pages =        "4611--4620",
  year =         "2015",
  ISSN =         "0957-4174",
  DOI =          "doi:10.1016/j.eswa.2015.01.065",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0957417415000883",
  abstract =     "One in every eight women is susceptible to breast
                 cancer, at some point of time in her life. Early
                 detection and effective treatment is the only rescue to
                 reduce breast cancer mortality. Accurate classification
                 of a breast cancer tumour is an important task in
                 medical diagnosis. Machine learning techniques are
                 gaining importance in medical diagnosis because of
                 their classification capability. In this paper, we
                 propose a new, Genetically Optimised Neural Network
                 (GONN) algorithm, for solving classification problems.
                 We evolve a neural network genetically to optimize its
                 architecture (structure and weight) for classification.
                 We introduce new crossover and mutation operators which
                 differ from standard crossover and mutation operators
                 to reduce the destructive nature of these operators. We
                 use the GONN algorithm to classify breast cancer tumors
                 as benign or malignant. To demonstrate our results, we
                 had taken the WBCD database from UCI Machine Learning
                 repository and compared the classification accuracy,
                 sensitivity, specificity, confusion matrix, ROC curves
                 and AUC under ROC curves of GONN with classical model
                 and classical back propagation model. Our algorithm
                 gives classification accuracy of 98.24percent,
                 99.63percent and 100percent for 50-50, 60-40, 70-30
                 training-testing partition respectively and 100percent
                 for 10 fold cross validation. The results show that our
                 approach works well with the breast cancer database and
                 can be a good alternative to the well-known machine
                 learning methods.",
  keywords =     "genetic algorithms, genetic programming, Genetically
                 Optimised Neural Network, Artificial Neural Network,
                 Modified Crossover Operator",
}

Genetic Programming entries for Arpit Bhardwaj Aruna Tiwari

Citations