Genetic programming outperformed multivariable logistic regression in diagnosing pulmonary embolism

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

@Article{Biesheuvel:2004:JCE,
  author =       "Cornelis J. Biesheuvel and Ivar Siccama and 
                 Diederick E. Grobbee and Karel G. M. Moons",
  title =        "Genetic programming outperformed multivariable
                 logistic regression in diagnosing pulmonary embolism",
  journal =      "Journal of Clinical Epidemiology",
  year =         "2004",
  volume =       "57",
  pages =        "551--560",
  number =       "6",
  abstract =     "Objective

                 Genetic programming is a search method that can be used
                 to solve complex associations between large numbers of
                 variables. It has been used, for example, for
                 myoelectrical signal recognition, but its value for
                 medical prediction as in diagnostic and prognostic
                 settings, has not been documented.

                 Study design and setting

                 We compared genetic programming and the commonly used
                 logistic regression technique in the development of a
                 prediction model using empirical data from a study on
                 diagnosis of pulmonary embolism. Using part (67%) of
                 the data, we developed and internally validated (using
                 bootstrapping techniques) a diagnostic prediction model
                 by genetic programming and by logistic regression, and
                 compared both on their predictive ability in the
                 remaining data (validation set).

                 Results

                 In the validation set, the area under the ROC curve of
                 the genetic programming model was significantly larger
                 (0.73; 95%CI: 0.64-0.82) than that of the logistic
                 regression model (0.68; 0.59-0.77). The calibration of
                 both models was similar, indicating a similar amount of
                 overoptimism.

                 Conclusion

                 Although the interpretation of a genetic programming
                 model is less intuitive and this is the first empirical
                 study quantifying its value for medical prediction,
                 genetic programming seems a promising technique to
                 develop prediction rules for diagnostic and prognostic
                 purposes.",
  owner =        "wlangdon",
  URL =          "http://igitur-archive.library.uu.nl/med/2006-0906-200235/grobbee_04_geneticprogrammingoutperformed.pdf",
  URL =          "http://www.sciencedirect.com/science/article/B6T84-4CTB5RT-3/2/325f5e3699d990701839201564eff8d3",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming, Logistic
                 regression, Prediction, Diagnostic research,
                 Discrimination, Reliability",
  DOI =          "doi:10.1016/j.jclinepi.2003.10.011",
  size =         "10 pages",
  notes =        "PMID: 15246123 [PubMed - indexed for MEDLINE]",
}

Genetic Programming entries for Cornelis Jan Biesheuvel Ivar Siccama Diederick E Grobbee Karel G M Moons

Citations