Applications of Machine Learning Models to Predict and Prevent Obesity: A Mini-Review

Zhou, Xiaobei; Chen, Lei; Liu, Hui‐Xin

doi:10.3389/fnut.2022.933130

Cited by 5 publications

(1 citation statement)

References 51 publications

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“…In such a context, reliable predictive models of childhood obesity integrated within the structure of the common well-child visits have the potential to provide timely risk alerts and inform more effective interventions to prevent and control this disease. Clinical predictive models for obesity, designed using artificial intelligence and machine learning (AI/ML) methods, are being considered to understand the contributing factors to the obesity epidemic and inform more effective interventions [9,10,11,12,13,14,15,16,17,18].…”

Section: Introductionmentioning

confidence: 99%

Reliable prediction of childhood obesity using only routinely collected EHRs is possible

Gupta,

Phan,

Eckrich

et al. 2024

Preprint

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ObjectiveIdentifying children at high risk of developing obesity can offer a critical time to change the course of the disease before it establishes. Numerous studies have tried to achieve this; but practical limitations remain, including (i) relying on data not present in routinely available pediatric data (like prenatal data), (ii) focusing on a single age prediction (hence, not tested across ages), and (iii) not achieving good results or adequately validating those.MethodsA customized sequential deep learning model was built to predict the risk of childhood obesity, focusing especially on capturing the temporal patterns. The model was trained only on routinely collected EHRs, containing a list of features identified by a group of clinical experts, and sourced from 36,191 diverse children aged 0 to 10. The model was evaluated using extensive discrimination, calibration, and utility analysis; and was validated temporally, geographically, and across various subgroups.ResultsOur results are mostly better (and never worse) than all previous studies, including those that focus on single-age predictions or link EHRs to external data. Specifically, the model consistently achieved an area under the curve (AUROC) of above 0.8 (with most cases around 0.9) for predicting obesity within the next 3 years for children 2 to 7. The validation results show the robustness of the model. Furthermore, the most influential predictors of the model match important risk factors of obesity.ConclusionsOur model is able to predict the risk of obesity for young children using only routinely collected EHR data, greatly facilitating its integration with the periodicity schedule. The model can serve as an objective screening tool to inform prevention efforts, especially by helping with very delicate interactions between providers and families in primary care settings.

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Section: Introductionmentioning

confidence: 99%

Reliable prediction of childhood obesity using only routinely collected EHRs is possible

Gupta,

Phan,

Eckrich

et al. 2024

Preprint

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Study of obesity research using machine learning methods: A bibliometric and visualization analysis from 2004 to 2023

Gong,

Bai,

Lei

et al. 2024

Medicine

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Background: Obesity, a multifactorial and complex health condition, has emerged as a significant global public health concern. Integrating machine learning techniques into obesity research offers great promise as an interdisciplinary field, particularly in the screening, diagnosis, and analysis of obesity. Nevertheless, the publications on using machine learning methods in obesity research have not been systematically evaluated. Hence, this study aimed to quantitatively examine, visualize, and analyze the publications concerning the use of machine learning methods in obesity research by means of bibliometrics. Methods: The Web of Science core collection was the primary database source for this study, which collected publications on obesity research using machine learning methods over the last 20 years from January 1, 2004, to December 31, 2023. Only articles and reviews that fit the criteria were selected for bibliometric analysis, and in terms of language, only English was accepted. VOSviewer, CiteSpace, and Excel were the primary software utilized. Results: Between 2004 and 2023, the number of publications on obesity research using machine learning methods increased exponentially. Eventually, 3286 publications that met the eligibility criteria were searched. According to the collaborative network analysis, the United States has the greatest volume of publications, indicating a significant influence on this research. coauthor’s analysis showed the authoritative one in this field is Leo Breiman. Scientific Reports is the most widely published journal. The most referenced publication is “R: a language and environment for statistical computing.” An analysis of keywords shows that deep learning, support vector machines, predictive models, gut microbiota, energy expenditure, and genome are hot topics in this field. Future research directions may include the relationship between obesity and its consequences, such as diabetic retinopathy, as well as the interaction between obesity and epidemiology, such as COVID-19. Conclusion: Utilizing bibliometrics as a research tool and methodology, this study, for the first time, reveals the intrinsic relationship and developmental pattern among obesity research using machine learning methods, which provides academic references for clinicians and researchers in understanding the hotspots and cutting-edge issues as well as the developmental trend in this field to detect patients’ obesity problems early and develop personalized treatment plans.

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Geospatial Modeling of Deep Neural Visual Features for Predicting Obesity Prevalence in Missouri: Quantitative Study

Dahu,

Khan,

Toubal

et al. 2024

JMIR AI

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Background The global obesity epidemic demands innovative approaches to understand its complex environmental and social determinants. Spatial technologies, such as geographic information systems, remote sensing, and spatial machine learning, offer new insights into this health issue. This study uses deep learning and spatial modeling to predict obesity rates for census tracts in Missouri. Objective This study aims to develop a scalable method for predicting obesity prevalence using deep convolutional neural networks applied to satellite imagery and geospatial analysis, focusing on 1052 census tracts in Missouri. Methods Our analysis followed 3 steps. First, Sentinel-2 satellite images were processed using the Residual Network-50 model to extract environmental features from 63,592 image chips (224×224 pixels). Second, these features were merged with obesity rate data from the Centers for Disease Control and Prevention for Missouri census tracts. Third, a spatial lag model was used to predict obesity rates and analyze the association between deep neural visual features and obesity prevalence. Spatial autocorrelation was used to identify clusters of obesity rates. Results Substantial spatial clustering of obesity rates was found across Missouri, with a Moran I value of 0.68, indicating similar obesity rates among neighboring census tracts. The spatial lag model demonstrated strong predictive performance, with an R2 of 0.93 and a spatial pseudo R2 of 0.92, explaining 93% of the variation in obesity rates. Local indicators from a spatial association analysis revealed regions with distinct high and low clusters of obesity, which were visualized through choropleth maps. Conclusions This study highlights the effectiveness of integrating deep convolutional neural networks and spatial modeling to predict obesity prevalence based on environmental features from satellite imagery. The model’s high accuracy and ability to capture spatial patterns offer valuable insights for public health interventions. Future work should expand the geographical scope and include socioeconomic data to further refine the model for broader applications in obesity research.

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Applications of Machine Learning Models to Predict and Prevent Obesity: A Mini-Review

Cited by 5 publications

References 51 publications

Reliable prediction of childhood obesity using only routinely collected EHRs is possible

Reliable prediction of childhood obesity using only routinely collected EHRs is possible

Study of obesity research using machine learning methods: A bibliometric and visualization analysis from 2004 to 2023

Geospatial Modeling of Deep Neural Visual Features for Predicting Obesity Prevalence in Missouri: Quantitative Study

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