ArticleBMC bioinformatics2018
Multiobjective grammar-based genetic programming applied to the study of asthma and allergy epidemiology.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 14 citations in OpenAlex.
- Spatial distribution of hospital admissions for asthma in the central area of Asturias, Northern Spain.BMC public health · 2023Article
- Post-Analysis of Predictive Modeling with an Epidemiological Example.Healthcare (Basel, Switzerland) · 2021Article
- Classification algorithm for congenital Zika Syndrome: characterizations, diagnosis and validation.Scientific reports · 2021Article
- Integrating Computational Methods to Investigate the Macroecology of Microbiomes.Frontiers in genetics · 2019Review
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Authors and funding
8 authors at 2 institutions in 2 countries.
Funding
Abstract
backgroundAsthma 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).
resultsThe 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.
conclusionsMGGP 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 .
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