Multiobjective grammar-based genetic programming applied to the study of asthma and allergy epidemiology.
BMC Bioinformatics
; 19(1): 245, 2018 06 26.
Article
em En
| MEDLINE
| ID: mdl-29940834
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 on repository http//bitbucket.org/ciml-ufjf/ciml-lib .Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Contexto em Saúde:
11_ODS3_cobertura_universal
/
2_ODS3
Problema de saúde:
11_multisectoral_coordination
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2_cobertura_universal
Assunto principal:
Asma
/
Alérgenos
/
Modelos Genéticos
Tipo de estudo:
Prognostic_studies
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Risk_factors_studies
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Screening_studies
Limite:
Humans
Idioma:
En
Revista:
BMC Bioinformatics
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2018
Tipo de documento:
Article
País de afiliação:
Brasil