Using Genetic Programming to Investigate a Novel Model of Resting Energy Expenditure for Bariatric Surgery Patients

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Hughes:2020:CIBCB,

• author = "James Alexander Hughes and Ryan E. R. Reid and Sheridan Houghten and Ross E. Andersen",
• title = "Using Genetic Programming to Investigate a Novel Model of Resting Energy Expenditure for Bariatric Surgery Patients",
• booktitle = "2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)",
• year = "2020",
• abstract = "Traditionally, models developed to estimate resting energy expenditure (REE) in the bariatric population have been limited to linear modelling based on data from `normal' or `overweight' individuals not `obese'. This type of modelling can be restrictive and yield functions which poorly estimate this important physiological outcome.Linear and nonlinear models of REE for individuals after bariatric surgery are developed with linear regression and symbolic regression via genetic programming. Features not traditionally used in REE modelling were also incorporated and analyzed and genetic programming's intrinsic feature selection was used as a measure of feature importance. A collection of effective new linear and nonlinear models were generated. The linear models generated outperformed the nonlinear on testing data, although the nonlinear models fit the training data better. Ultimately, the newly developed linear models showed an improvement over existing models and the feature importance analysis suggested that the typically used features (age, weight, and height) were the most important.",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/CIBCB48159.2020.9277696",
• month = oct,
• notes = "Also known as \cite{9277696}",

}

Genetic Programming entries for James Alexander Hughes Ryan E R Reid Sheridan Houghten Ross E Andersen

Citations