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

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@Article{Veiga:2018:BMCbi,

• 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 epidemiology",
• 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 interpret.

  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 = "http://bitbucket.org/ciml-ufjf/ciml-lib

  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

Citations