Application of gene expression programming and neural networks to predict adverse events of radical hysterectomy in cervical cancer patients
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{Kusy:2013:MBEC,
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author = "Maciej Kusy and Bogdan Obrzut and Jacek Kluska",
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title = "Application of gene expression programming and neural
networks to predict adverse events of radical
hysterectomy in cervical cancer patients",
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journal = "Medical \& Biological Engineering \& Computing",
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year = "2013",
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volume = "51",
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number = "12",
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pages = "1357--1365",
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publisher = "Springer",
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month = "1 " # dec,
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keywords = "genetic algorithms, genetic programming, gene
expression programming, GEP, cervical cancer, radical
hysterectomy, perioperative complications, neural
networks",
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ISSN = "0140-0118",
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bibsource = "OAI-PMH server at www.ncbi.nlm.nih.gov",
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language = "English",
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oai = "oai:pubmedcentral.nih.gov:3825140",
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oai = "oai:CiteSeerX.psu:10.1.1.1029.4333",
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URL = "http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3825140",
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URL = "http://www.ncbi.nlm.nih.gov/pubmed/24136688",
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URL = "http://dx.doi.org/10.1007/s11517-013-1108-8",
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URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1029.4333",
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DOI = "doi:10.1007/s11517-013-1108-8",
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size = "9 pages",
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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