Article
Multiobjective grammar-based genetic programming applied to the study of asthma and allergy epidemiology
Registro en:
VEIGA, R. V. et al. Multiobjective grammar-based genetic programming applied to the study of asthma and allergy epidemiology. BMC Bioinformatics, v. 19, n. 245, p. 1-16, 2018.
1471-2105
10.1186/s12859-018-2233-z
Autor
Veiga, Rafael Valente
Barbosa, Helio José Correa
Bernardino, Heder Soares
Freitas, João M
Feitosa, Caroline Alves
Matos, Sheila Maria Alvim de
Neves, Neuza Maria Alcântara
Barreto, Maurício Lima
Resumen
Wellcome Trust (grant 072405/Z/03/Z).
The Wellcome Trust was not involved in the design of the study, analysis, and
interpretation of data, and in writing the manuscript. The following agencies
from the brazilian government gave support to the researchers: CAPES, CNPq
(grant 310778/2013-1), FAPEMIG (grant APQ-03414-15). RVV acknowledges a
PhD scholarship from CAPES and is currently recipient of a post-doctorate
grant from CNPq (438732/2016-2). 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 on repository: http://bitbucket.org/cimlufjf/ciml-lib.