Examining the utility of nonlinear machine learning approaches versus linear regression for predicting body image outcomes: The U.S. Body Project I.

Body Image / psychology Humans Linear Models Machine Learning
Body image Deep neural networks Machine learning Random forest Tripartite model

Journal

Body image
ISSN: 1873-6807
Titre abrégé: Body Image
Pays: Netherlands
ID NLM: 101222431

Informations de publication

Date de publication:
Jun 2022
Historique:
received: 16 12 2020
revised: 14 12 2021
accepted: 26 01 2022
pubmed: 2 3 2022
medline: 7 6 2022
entrez: 1 3 2022
Statut: ppublish

Résumé

Most body image studies assess only linear relations between predictors and outcome variables, relying on techniques such as multiple Linear Regression. These predictor variables are often validated multi-item measures that aggregate individual items into a single scale. The advent of machine learning has made it possible to apply Nonlinear Regression algorithms-such as Random Forest and Deep Neural Networks-to identify potentially complex linear and nonlinear connections between a multitude of predictors (e.g., all individual items from a scale) and outcome (output) variables. Using a national dataset, we tested the extent to which these techniques allowed us to explain a greater share of the variance in body-image outcomes (adjusted R

Identifiants

DOI: 10.1016/j.bodyim.2022.01.013 PMID: 35228102
pubmed: 35228102
pii: S1740-1445(22)00012-2
doi: 10.1016/j.bodyim.2022.01.013
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

32-45

Informations de copyright

Copyright © 2022 Elsevier Ltd. All rights reserved.

Auteurs

Dehua Liang (D)

Fowler School of Engineering, Chapman University, Orange, CA, USA; Schmid College of Sciences and Technology, Chapman University, Orange, CA, USA.

David A Frederick (DA)

Crean College of Health and Behavioral Sciences, Chapman University, Orange, CA, USA. Electronic address: dfrederi@chapman.edu.

Elia E Lledo (EE)

Fowler School of Engineering, Chapman University, Orange, CA, USA.

Natalia Rosenfield (N)

Fowler School of Engineering, Chapman University, Orange, CA, USA.

Vincent Berardi (V)

Fowler School of Engineering, Chapman University, Orange, CA, USA; Crean College of Health and Behavioral Sciences, Chapman University, Orange, CA, USA.

Erik Linstead (E)

Fowler School of Engineering, Chapman University, Orange, CA, USA.

Uri Maoz (U)

Fowler School of Engineering, Chapman University, Orange, CA, USA; Schmid College of Sciences and Technology, Chapman University, Orange, CA, USA; Crean College of Health and Behavioral Sciences, Chapman University, Orange, CA, USA; Institute for Interdisciplinary Brain and Behavioral Sciences, Chapman University, Orange, CA, USA.

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Classifications MeSH

questionsmedicales.fr › Phénomènes psychologiques › Processus mentaux › Perception › Image du corps › Examining the utility of nonlinear machine...
questionsmedicales.fr › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Primates › Haplorhini › Catarrhini › Hominidae › Humains › Examining the utility of nonlinear machine...
questionsmedicales.fr › Environnement et santé publique › Santé publique › Méthodes épidémiologiques › Statistiques comme sujet › Analyse de régression › Modèles linéaires › Examining the utility of nonlinear machine...
questionsmedicales.fr › Sciences de l'information › Méthodologies informatiques › Algorithmes › Intelligence artificielle › Apprentissage machine › Examining the utility of nonlinear machine...