Multiobjective grammar-based genetic programming applied to the study of asthma and allergy epidemiology

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  author =       "Rafael V. Veiga and Helio J. C. Barbosa and 
                 Heder S. Bernardino and Joao M. Freitas and 
                 Caroline A. Feitosa and Sheila M. A. Matos and Neuza M. Alcantara-Neves and 
                 Mauricio L. Barreto",
  title =        "Multiobjective grammar-based genetic programming
                 applied to the study of asthma and allergy
  journal =      "BMC Bioinformatics",
  year =         "2018",
  volume =       "19",
  number =       "245",
  keywords =     "genetic algorithms, genetic programming, Asthma,
                 Allergy, Classifier",
  DOI =          "doi:10.1186/s12859-018-2233-z",
  size =         "16 pages",
  abstract =     "Background: Asthma and allergies prevalence increased
                 in recent decades, being a serious global health
                 problem. They are complex diseases with strong
                 contextual influence, so that the use of advanced
                 machine learning tools such as genetic programming
                 could be important for the understanding the causal
                 mechanisms explaining those conditions. Here, we
                 applied a multiobjective grammar-based genetic
                 programming (MGGP) to a dataset composed by 1047
                 subjects. The dataset contains information on the
                 environmental, psychosocial, socioeconomics,
                 nutritional and infectious factors collected from
                 participating children. The objective of this work is
                 to generate models that explain the occurrence of
                 asthma, and two markers of allergy: presence of IgE
                 antibody against common allergens, and skin prick test
                 positivity for common allergens (SPT).

                 Results: The average of the accuracies of the models
                 for asthma higher in MGGP than C4.5. IgE were higher in
                 MGGP than in both, logistic regression and C4.5. MGGP
                 had levels of accuracy similar to RF, but unlike RF,
                 MGGP was able to generate models that were easy to

                 Conclusions: MGGP has shown that infections,
                 psychosocial, nutritional, hygiene, and socioeconomic
                 factors may be related in such an intricate way, that
                 could be hardly detected using traditional regression
                 based epidemiological techniques. The algorithm MGGP
                 was implemented in c++ and is available",
  notes =        "

                 Methodology article Open Access",

Genetic Programming entries for Rafael Valente Veiga Helio J C Barbosa Heder Soares Bernardino Joao Marcos de Freitas Caroline Alves Feitosa Sheila M A Matos Neuza Maria Alcantara-Neves Mauricio Lima Barreto