Work

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

Year: 2020

Type: conference-paper

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.

Source: IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)

DOI: 10.1109/cibcb48159.2020.9277696

Authors: James Alexander Hughes, Ryan E. R. Reid, Sheridan Houghten, Ross E. Andersen

Institutions: St. Francis Xavier University, Brock University, McGill University

Language: English

Topic: Bariatric Surgery and Outcomes

Subfield: Surgery

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

Funder: Natural Sciences and Engineering Research Council of Canada