Feasibility of a screening tool for obesity diagnosis in Mexican children from a vulnerable community of Me'Phaa ethnicity in the State of Guerrero, Mexico

Rios-Julian, N.; Alarcón-Paredes, Antonio; Alonso, Gustavo; Hernández-Rosales, Diana E; Guzmán‐Guzmán, Iris Paola

doi:10.1109/gmepe-pahce.2017.7972105

Cited by 3 publications

(4 citation statements)

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“…The former models use traditional statistical techniques, mainly logistic regression, [ 34 , 35 , 36 , 37 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 ] although there are cases using linear regression [ 40 ], quantile regression [ 38 ], and ordinal logistic regression. [ 47 ] The ML models use a wide variety of ML methods: ANN [ 56 , 57 , 58 , 67 , 68 , 70 , 75 ], SVM [ 58 , 66 , 67 ], DT [ 58 , 64 , 65 , 67 , 68 , 69 , 70 , 73 ], NB [ 58 , 60 , 61 , 62 , 66 , 67 ], BN [ 58 , 65 , 67 , 76 ], LASSO [ 72 , 74 ], kNN [ 70 ], RF [ 59 , 65 , 68 , 72 ], GBM [ 72 , 77 ], and DL (RNN […”

Section: Discussionmentioning

confidence: 99%

“…In general, when in the same work logistic/linear regression is compared with ML models when fitting the same dataset [ 58 , 64 , 68 , 70 , 72 , 75 ], the latter give better results than the former in terms of prediction performance. This confirms that ML techniques are able to yield better predictions, not just by fitting better the training set but also through giving better results in internal and/or external validations.…”

Section: Discussionmentioning

confidence: 99%

“…A later paper in the same year by Rios-Julián et al [ 68 ] attempted to predict obesity + overweight (following the CDC criteria) vs. normal-weight by using BMI and other anthropometric variables in a community of Me’Phaa ethnicity in Mexico. They modeled a cohort of 221 children 6–13 years old by using different ML models: J48, logistic model trees, ANN, RF, and logistic regression.…”

Section: Machine Learning Models To Predict Childhood/adolescent Omentioning

confidence: 99%

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Machine Learning Models to Predict Childhood and Adolescent Obesity: A Review

Colmenarejo

2020

Nutrients

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The prevalence of childhood and adolescence overweight an obesity is raising at an alarming rate in many countries. This poses a serious threat to the current and near-future health systems, given the association of these conditions with different comorbidities (cardiovascular diseases, type II diabetes, and metabolic syndrome) and even death. In order to design appropriate strategies for its prevention, as well as understand its origins, the development of predictive models for childhood/adolescent overweight/obesity and related outcomes is of extreme value. Obesity has a complex etiology, and in the case of childhood and adolescence obesity, this etiology includes also specific factors like (pre)-gestational ones; weaning; and the huge anthropometric, metabolic, and hormonal changes that during this period the body suffers. In this way, Machine Learning models are becoming extremely useful tools in this area, given their excellent predictive power; ability to model complex, nonlinear relationships between variables; and capacity to deal with high-dimensional data typical in this area. This is especially important given the recent appearance of large repositories of Electronic Health Records (EHR) that allow the development of models using datasets with many instances and predictor variables, from which Deep Learning variants can generate extremely accurate predictions. In the current work, the area of Machine Learning models to predict childhood and adolescent obesity and related outcomes is comprehensively and critically reviewed, including the latest ones using Deep Learning with EHR. These models are compared with the traditional statistical ones that used mainly logistic regression. The main features and applications appearing from these models are described, and the future opportunities are discussed.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Machine Learning Models To Predict Childhood/adolescent Omentioning

confidence: 99%

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Machine Learning Models to Predict Childhood and Adolescent Obesity: A Review

Colmenarejo

2020

Nutrients

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“…Using anthropometric data, Rios-Julian et al . [ 25 ] estimated the viability of creating an automated screening tool for identifying obesity. Dugan et al .…”

Section: Resultsmentioning

confidence: 99%

Applications of artificial intelligence in the management of childhood obesity

Alghalyini

2023

Journal of Family Medicine and Primary Care

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Background: Childhood obesity has emerged as a significant public health challenge, with long-term implications that often extend into adulthood, increasing the susceptibility to chronic health conditions. Objective: The objective of this review is to elucidate the applications of artificial intelligence (AI) in the prevention and treatment of pediatric obesity, emphasizing its potential to complement and enhance traditional management methods. Methods: We undertook a comprehensive examination of existing literature to understand the integration of machine learning and other AI techniques in childhood obesity management strategies. Results: The findings from numerous studies suggest a strong endorsement for AI's role in addressing childhood obesity. Particularly, machine learning techniques have shown considerable efficacy in augmenting current therapeutic and preventive approaches. Conclusion: The intersection of AI with conventional obesity management practices presents a novel and promising approach to fortify interventions targeting pediatric obesity. This review accentuates the transformative capacity of AI, thereby advocating for continued research and innovation in this rapidly evolving domain.

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Efficacy of Emerging Technologies to Manage Childhood Obesity

Alotaibi¹,

Alnajjar²,

Cappuccio³

et al. 2022

DMSO

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Childhood obesity is a widespread medical condition and presents a formidable challenge for public health. Long-term treatment strategies and early prevention strategies are required because obese children are more likely to carry this condition into adulthood, increasing their risk of developing other major health disorders. The present review analyses various technological interventions available for childhood obesity prevention and treatment. It also examines whether machine learning and technological interventions can play vital roles in its management. Twenty-six studies were shortlisted for the review using various technological strategies and analysed regarding their efficacy. While most of the selected studies showed positive outcomes, there was a lack of studies using robots and artificial intelligence to manage obesity in children. The use of machine learning was observed in various studies, and the integration of social robots and other efficacious strategies may be effective for treating childhood obesity in the future.

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Feasibility of a screening tool for obesity diagnosis in Mexican children from a vulnerable community of Me'Phaa ethnicity in the State of Guerrero, Mexico

Cited by 3 publications

References 14 publications

Machine Learning Models to Predict Childhood and Adolescent Obesity: A Review

Machine Learning Models to Predict Childhood and Adolescent Obesity: A Review

Applications of artificial intelligence in the management of childhood obesity

Efficacy of Emerging Technologies to Manage Childhood Obesity

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