Random forests for automatic differential diagnosis of erythemato-squamous diseases

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@Article{El-Bachir-Menai:2015:IJMEI,
  author =       "Mohamed {El Bachir Menai}",
  title =        "Random forests for automatic differential diagnosis of
                 erythemato-squamous diseases",
  journal =      "International Journal of Medical Engineering and
                 Informatics",
  publisher =    "Inderscience Publishers",
  year =         "2015",
  month =        apr # "~04",
  volume =       "7",
  number =       "2",
  pages =        "124--141",
  ISSN =         "1755-0661",
  keywords =     "genetic algorithms, genetic programming,
                 erythemato-squamous diseases, ESD, automatic
                 differential diagnosis, decision trees, random forests,
                 boosting, skin diseases, dermatology, classifiers",
  bibsource =    "OAI-PMH server at www.inderscience.com",
  URL =          "http://www.inderscience.com/link.php?id=68506",
  DOI =          "doi:10.1504/IJMEI.2015.068506",
  abstract =     "Erythemato-squamous diseases (ESD) are frequent skin
                 diseases that share some clinical features of erythema
                 and scaling. Their automatic diagnosis was tackled
                 using several approaches that achieved high performance
                 accuracy. However, they generally remained unattractive
                 for dermatologists because of the lack of direct
                 readability of their output models. Decision trees are
                 easy to understand, but their performance and structure
                 are very sensitive to data changes. Ensembles of
                 decision trees were introduced to reduce the effect of
                 these problems, but on the expense of interpretability.
                 In this paper, we present the results of our
                 investigation of random forests and boosting as
                 ensemble methods for the differential diagnosis of ESD.
                 Experiments on clinical and histopathological data
                 showed that the random forest outperformed the other
                 ensemble classifiers in terms of accuracy, sensitivity
                 and specificity. Its diagnosis accuracy, attaining more
                 than 98percent, was also better than those of
                 classifiers based on genetic programming, genetic
                 algorithms and k-means clustering.",
}

Genetic Programming entries for Mohamed El Bachir Menai

Citations