A review of the application of deep learning in obesity: From early prediction aid to advanced management assistance

BACKGROUND AND AIMS

Obesity is a chronic disease which can cause severe metabolic disorders. Machine learning (ML) techniques, especially deep learning (DL), have proven to be useful in obesity research. However, there is a dearth of systematic reviews of DL applications in obesity. This article aims to summarize the current trend of DL usage in obesity research.

METHODS

An extensive literature review was carried out across multiple databases, including PubMed, Embase, Web of Science, Scopus, and Medline, to collate relevant studies published from January 2018 to September 2023. The focus was on research detailing the application of DL in the context of obesity. We have distilled critical insights pertaining to the utilized learning models, encompassing aspects of their development, principal results, and foundational methodologies.

RESULTS

Our analysis culminated in the synthesis of new knowledge regarding the application of DL in the context of obesity. Finally, 40 research articles were included. The final collection of these research can be divided into three categories: obesity prediction (n = 16); obesity management (n = 13); and body fat estimation (n = 11).

CONCLUSIONS

This is the first review to examine DL applications in obesity. It reveals DL's superiority in obesity prediction over traditional ML methods, showing promise for multi-omics research. DL also innovates in obesity management through diet, fitness, and environmental analyses. Additionally, DL improves body fat estimation, offering affordable and precise monitoring tools. The study is registered with PROSPERO (ID: CRD42023475159).

Obesity prediction by modelling BMI distributions: application to national survey data from Mexico, Colombia and Peru, 1988-2014

BACKGROUND

The prediction of future obesity patterns is crucial for effective strategic planning. However, disproportionally changing body mass index (BMI) distributions pose particular challenges. Flexible modelling of the shape of BMI distributions may improve prediction performance.

METHODS

We used data from repeated national health surveys conducted in Mexico, Colombia and Peru at four or five time points between 1988 and 2014. Data from all surveys except the last survey were used to construct prediction models for three obesity indicators (median BMI, overweight/obesity prevalence and obesity prevalence) for the time of the last survey. We assessed their performance using predicted curves, absolute prediction errors and comparison of actual and predicted distributions. With one method, we modelled the shape of BMI distributions assuming BMI follows a Box-Cox Power Exponential (BCPE) distribution, whose parameters were modelled as a function of interval or nominal 5-year age groups, time and their interaction terms. In a second method, we modelled each of the obesity indicators directly as a function of the same covariates using quantile and logistic regression.

RESULTS

The BCPE model with interval age groups yielded the best prediction performance in predicting obesity prevalence. Average absolute prediction errors across all age groups were 4.3 percentage points (95% percentile interval: 1.9, 7.5), 2.5 (1.2, 6.1) and 1.7 (1.0, 9.3), with data from Mexico, Colombia and Peru, respectively. This superiority was weak or none for overweight/obesity prevalence and median BMI.

CONCLUSION

The BCPE model performed better for prediction of the extremes of BMI distribution, possibly by incorporating its shape more precisely.