A Survey on Machine and Deep Learning Models for Childhood and Adolescent Obesity

Childhood and adolescent obesity is a serious health problem that is on the rise at the global level. Earlier, certain diseases such as Type 2 diabetes, high blood pressure, and heart disease affected only adults, but now they are being detected in young children as well. Several studies based on ma...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Hera Siddiqui, Ajita Rattani, Nikki K. Woods, Laila Cure, Rhonda K. Lewis, Janet Twomey, Betty Smith-Campbell, Twyla J. Hill
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Adolescent obesity childhood obesity deep learning machine learning obesity prediction key determinants Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/13942c989c624a07bc0c204f8dffdd8f
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:13942c989c624a07bc0c204f8dffdd8f
record_format	dspace
spelling	oai:doaj.org-article:13942c989c624a07bc0c204f8dffdd8f2021-12-03T00:00:36ZA Survey on Machine and Deep Learning Models for Childhood and Adolescent Obesity2169-353610.1109/ACCESS.2021.3131128https://doaj.org/article/13942c989c624a07bc0c204f8dffdd8f2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9627712/https://doaj.org/toc/2169-3536Childhood and adolescent obesity is a serious health problem that is on the rise at the global level. Earlier, certain diseases such as Type 2 diabetes, high blood pressure, and heart disease affected only adults, but now they are being detected in young children as well. Several studies based on machine learning have been proposed to develop obesity prediction models or to determine key determinants of obesity for designing intervention tools. Despite having a rich and diverse set of literature on obesity prediction models, obesity rates are at an all-time high for both children and adolescents. There is a need of proper understanding and critical analysis of existing machine learning models in order to design effective strategies for curbing obesity at childhood and adolescent level. This paper surveys the growing body of recent literature on machine and deep learning models for obesity prediction by providing a coherent view (critical analysis) of the limitations of the existing systems. The taxonomy of the existing literature on obesity prediction into methods used, predicted outcome, factors used, type of datasets, and the associated purpose, is discussed for analysis of the state-of-the-art. This analysis revealed that a) prediction-focused models do not use variables from as many domains as predictor-focused models do, b) very few studies proposed gender-specific and race-specific obesity prediction models, c) lack of large-scale multimodal datasets and d) existing predictor-focused models obtain an accuracy range of [53.7%, 96%] with an optimum set of predictors. Further, computer vision-based methods for obesity prediction and interpretable techniques for understanding the outcome of the models are discussed as well. In addition, we have also identified novel research directions. The overall aim is to advance the state-of-the-art and improve the quality of discourse in this field.Hera SiddiquiAjita RattaniNikki K. WoodsLaila CureRhonda K. LewisJanet TwomeyBetty Smith-CampbellTwyla J. HillIEEEarticleAdolescent obesitychildhood obesitydeep learningmachine learningobesity predictionkey determinantsElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 157337-157360 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Adolescent obesity childhood obesity deep learning machine learning obesity prediction key determinants Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Adolescent obesity childhood obesity deep learning machine learning obesity prediction key determinants Electrical engineering. Electronics. Nuclear engineering TK1-9971 Hera Siddiqui Ajita Rattani Nikki K. Woods Laila Cure Rhonda K. Lewis Janet Twomey Betty Smith-Campbell Twyla J. Hill A Survey on Machine and Deep Learning Models for Childhood and Adolescent Obesity
description	Childhood and adolescent obesity is a serious health problem that is on the rise at the global level. Earlier, certain diseases such as Type 2 diabetes, high blood pressure, and heart disease affected only adults, but now they are being detected in young children as well. Several studies based on machine learning have been proposed to develop obesity prediction models or to determine key determinants of obesity for designing intervention tools. Despite having a rich and diverse set of literature on obesity prediction models, obesity rates are at an all-time high for both children and adolescents. There is a need of proper understanding and critical analysis of existing machine learning models in order to design effective strategies for curbing obesity at childhood and adolescent level. This paper surveys the growing body of recent literature on machine and deep learning models for obesity prediction by providing a coherent view (critical analysis) of the limitations of the existing systems. The taxonomy of the existing literature on obesity prediction into methods used, predicted outcome, factors used, type of datasets, and the associated purpose, is discussed for analysis of the state-of-the-art. This analysis revealed that a) prediction-focused models do not use variables from as many domains as predictor-focused models do, b) very few studies proposed gender-specific and race-specific obesity prediction models, c) lack of large-scale multimodal datasets and d) existing predictor-focused models obtain an accuracy range of [53.7%, 96%] with an optimum set of predictors. Further, computer vision-based methods for obesity prediction and interpretable techniques for understanding the outcome of the models are discussed as well. In addition, we have also identified novel research directions. The overall aim is to advance the state-of-the-art and improve the quality of discourse in this field.
format	article
author	Hera Siddiqui Ajita Rattani Nikki K. Woods Laila Cure Rhonda K. Lewis Janet Twomey Betty Smith-Campbell Twyla J. Hill
author_facet	Hera Siddiqui Ajita Rattani Nikki K. Woods Laila Cure Rhonda K. Lewis Janet Twomey Betty Smith-Campbell Twyla J. Hill
author_sort	Hera Siddiqui
title	A Survey on Machine and Deep Learning Models for Childhood and Adolescent Obesity
title_short	A Survey on Machine and Deep Learning Models for Childhood and Adolescent Obesity
title_full	A Survey on Machine and Deep Learning Models for Childhood and Adolescent Obesity
title_fullStr	A Survey on Machine and Deep Learning Models for Childhood and Adolescent Obesity
title_full_unstemmed	A Survey on Machine and Deep Learning Models for Childhood and Adolescent Obesity
title_sort	survey on machine and deep learning models for childhood and adolescent obesity
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/13942c989c624a07bc0c204f8dffdd8f
work_keys_str_mv	AT herasiddiqui asurveyonmachineanddeeplearningmodelsforchildhoodandadolescentobesity AT ajitarattani asurveyonmachineanddeeplearningmodelsforchildhoodandadolescentobesity AT nikkikwoods asurveyonmachineanddeeplearningmodelsforchildhoodandadolescentobesity AT lailacure asurveyonmachineanddeeplearningmodelsforchildhoodandadolescentobesity AT rhondaklewis asurveyonmachineanddeeplearningmodelsforchildhoodandadolescentobesity AT janettwomey asurveyonmachineanddeeplearningmodelsforchildhoodandadolescentobesity AT bettysmithcampbell asurveyonmachineanddeeplearningmodelsforchildhoodandadolescentobesity AT twylajhill asurveyonmachineanddeeplearningmodelsforchildhoodandadolescentobesity AT herasiddiqui surveyonmachineanddeeplearningmodelsforchildhoodandadolescentobesity AT ajitarattani surveyonmachineanddeeplearningmodelsforchildhoodandadolescentobesity AT nikkikwoods surveyonmachineanddeeplearningmodelsforchildhoodandadolescentobesity AT lailacure surveyonmachineanddeeplearningmodelsforchildhoodandadolescentobesity AT rhondaklewis surveyonmachineanddeeplearningmodelsforchildhoodandadolescentobesity AT janettwomey surveyonmachineanddeeplearningmodelsforchildhoodandadolescentobesity AT bettysmithcampbell surveyonmachineanddeeplearningmodelsforchildhoodandadolescentobesity AT twylajhill surveyonmachineanddeeplearningmodelsforchildhoodandadolescentobesity
_version_	1718373973486469120

A Survey on Machine and Deep Learning Models for Childhood and Adolescent Obesity

Ejemplares similares