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Systematic Review of Machine Learning applied to the Prediction of Obesity and Overweight

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A Correction to this article was published on 17 February 2023

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Abstract

Obesity and overweight has increased in the last year and has become a pandemic disease, the result of sedentary lifestyles and unhealthy diets rich in sugars, refined starches, fats and calories. Machine learning (ML) has proven to be very useful in the scientific community, especially in the health sector. With the aim of providing useful tools to help nutritionists and dieticians, research focused on the development of ML and Deep Learning (DL) algorithms and models is searched in the literature. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol has been used, a very common technique applied to carry out revisions. In our proposal, 17 articles have been filtered in which ML and DL are applied in the prediction of diseases, in the delineation of treatment strategies, in the improvement of personalized nutrition and more. Despite expecting better results with the use of DL, according to the selected investigations, the traditional methods are still the most used and the yields in both cases fluctuate around positive values, conditioned by the databases (transformed in each case) to a greater extent than by the artificial intelligence paradigm used. Conclusions: An important compilation is provided for the literature in this area. ML models are time-consuming to clean data, but (like DL) they allow automatic modeling of large volumes of data which makes them superior to traditional statistics.

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  1. IoT: Internet of Things

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Acknowledgements

We thank to the project Digital Lab for Education in Dietetics combining Experiential Learning and Community Service for opening up such an interesting research path for us.

Funding

The article was written in the framework of the Erasmus + project “Digital Lab for Education in Dietetics combining Experiential Learning and Community Service” (Dieting-lab, 2021-2024, nº 2021-1-ES01-KA220-HED-000032074). This paper has been funded by the Spanish Service for the Internationalization of Education (SEPIE, European Union).

Author information

Authors and Affiliations

  1. Department of Signal Theory and Communications and Telematics Engineering, University of Valladolid, Paseo de Belén 15, Valladolid, 47011, Spain

    Antonio Ferreras

  2. Research Group on Foods, Nutritional Biochemistry and Health, European University of the Atlantic, Santander, 39011, Spain

    Sandra Sumalla-Cano

  3. Department of Health, Nutrition and Sport, Iberoamerican International University, Campeche, 24560, Mexico

    Sandra Sumalla-Cano

  4. Telemedicine and eHealth Research Group, Department of Signal Theory and Communications and Telematics Engineering, University of Valladolid, Paseo de Belén 15, Valladolid, 47011, Spain

    Rosmeri Martínez-Licort

  5. Department of Telecommunications, University of Pinar del Río, Pinar del Río, Cuba

    Rosmeri Martínez-Licort

  6. Research Group on Foods, Nutritional Biochemistry and Health, European University of the Atlantic, Santander, 39011, Spain

    Iñaki Elío

  7. Department of Health, Nutrition and Sport, Iberoamerican International University, Campeche, 24560, Mexico

    Iñaki Elío

  8. Higher Polytechnic School, European University of the Atlantic, Santander, 39011, Spain

    Kilian Tutusaus & Juan Luís Vidal-Mazón

  9. Higher Polytechnic School, Iberoamerican International University, Campeche, 24560, Mexico

    Kilian Tutusaus

  10. Faculty of Social Sciences and Humanites, European University of the Atlantic, Santander, Spain

    Thomas Prola

  11. Higher Polytechnic School, International University of Cuanza, Estrada nacional 250, Cuito-Bié, Angola

    Juan Luís Vidal-Mazón

  12. Higher Polytechnic School, Iberoamerican International University, Arecibo, 00613, Puerto Rico

    Juan Luís Vidal-Mazón

  13. Research group GCME, Department of Computer Science, University of Valladolid, Paseo de Belén 15, Valladolid, 47011, Spain

    Benjamín Sahelices

  14. Department of Signal Theory and Communications and Telematics Engineering, University of Valladolid, Paseo de Belén 15, Valladolid, 47011, Spain

    Isabel de la Torre Díez

Authors
  1. Antonio Ferreras
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  9. Isabel de la Torre Díez
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Contributions

All authors contributed to the design and conduct of the study. The idea of the investigation has been conceived by Iñaki Elío and Isabel de la Torre Díez, who have conditioned its realization. The first draft of the manuscript was written by Antonio Ferreras, Sandra Sumalla-Cano, Isabel de la Torre Díez and Iñaki Elío. The latest version and the graphics were prepared by Rosmeri Martínez Licort and Iñaki Elío. The review and completion of the entire report has been carried out by Kilian Tutusaus, Thomas Prola, Juan Luís Vidal-Mazón and Benjamín Sahelices. All authors read and approved the final manuscript. All authors participated in the coordination of the research, contributed to the writing and approved the final document.

Corresponding author

Correspondence to Rosmeri Martínez-Licort.

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The original online version of this article was revised: The author name "Iñaki" was misspelled as "Iñaka" in the original publication of this article.

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Ferreras, A., Sumalla-Cano, S., Martínez-Licort, R. et al. Systematic Review of Machine Learning applied to the Prediction of Obesity and Overweight. J Med Syst 47, 8 (2023). https://doi.org/10.1007/s10916-022-01904-1

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