Application of gene expression programming and neural networks to predict adverse events of radical hysterectomy in cervical cancer patients

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@Article{Kusy:2013:MBEC,
  author =       "Maciej Kusy and Bogdan Obrzut and Jacek Kluska",
  title =        "Application of gene expression programming and neural
                 networks to predict adverse events of radical
                 hysterectomy in cervical cancer patients",
  journal =      "Medical \& Biological Engineering \& Computing",
  year =         "2013",
  volume =       "51",
  number =       "12",
  pages =        "1357--1365",
  publisher =    "Springer",
  month =        "1 " # dec,
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming, GEP, cervical cancer, radical
                 hysterectomy, perioperative complications, neural
                 networks",
  ISSN =         "0140-0118",
  bibsource =    "OAI-PMH server at www.ncbi.nlm.nih.gov",
  language =     "English",
  oai =          "oai:pubmedcentral.nih.gov:3825140",
  oai =          "oai:CiteSeerX.psu:10.1.1.1029.4333",
  URL =          "http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3825140",
  URL =          "http://www.ncbi.nlm.nih.gov/pubmed/24136688",
  URL =          "http://dx.doi.org/10.1007/s11517-013-1108-8",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1029.4333",
  DOI =          "doi:10.1007/s11517-013-1108-8",
  size =         "9 pages",
  abstract =     "The aim of this article was to compare gene expression
                 programming (GEP) method with three types of neural
                 networks in the prediction of adverse events of radical
                 hysterectomy in cervical cancer patients. One-hundred
                 and seven patients treated by radical hysterectomy were
                 analysed. Each record representing a single patient
                 consisted of 10 parameters. The occurrence and lack of
                 perioperative complications imposed a two-class
                 classification problem. In the simulations, GEP
                 algorithm was compared to a multilayer perceptron
                 (MLP), a radial basis function network neural, and a
                 probabilistic neural network. The generalisation
                 ability of the models was assessed on the basis of
                 their accuracy, the sensitivity, the specificity, and
                 the area under the receiver operating characteristic
                 curve (AUROC). The GEP classifier provided best results
                 in the prediction of the adverse events with the
                 accuracy of 71.96percent. Comparable but slightly worse
                 outcomes were obtained using MLP, i.e., 71.87percent.
                 For each of measured index: accuracy, sensitivity,
                 specificity, and the AUROC, the standard deviation was
                 the smallest for the models generated by GEP
                 classifier.",
}

Genetic Programming entries for Maciej Kusy Bogdan Obrzut Jacek Kluska

Citations