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Nutrition, Data Science and Artificial Intelligence

The Nutrition, Data Science, and Artificial Intelligence special collection aims to explore the intersection of nutrition science, data analytics, and AI. We invite submissions that leverage advanced computational techniques to analyse large-scale dietary data, predict nutritional outcomes, and develop innovative solutions for personalised dietary recommendations and dietary pattern analysis.
 

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

Details
Category: Nutrition, Data Science and Artificial Intelligence
Hits: 45
ID: 101
  • Study Status: Published
  • Study Type: Review
  • Study Location: Spain

Study Title
Systematic Review of Machine Learning Applied to the Prediction of Obesity and Overweight

Principal Investigator
Antonio Ferreras, Sandra Sumalla-Cano, Rosmeri Martínez-Licort, Iñaki Elío, Kilian Tutusaus, Thomas Prola, Juan Luís Vidal-Mazón, Benjamín Sahelices, Isabel de la Torre Díez

Affiliation
University of Valladolid, Spain; European University of the Atlantic, Spain; Iberoamerican International University, Mexico; University of Pinar del Río, Cuba

Start Date
Not specified

End Date
Not specified

Study Objective
To explore machine learning (ML) applications in predicting obesity and overweight through systematic analysis of existing literature.

Short Abstract
This systematic review evaluates various machine learning techniques applied to the prediction of obesity and overweight. The study focuses on the effectiveness of these models in different populations and identifies key challenges and opportunities in applying ML to obesity prediction.

Study Design
Systematic review

Population
Various populations with a focus on predictive models for obesity and overweight.

Sample Size
Not specified

Inclusion Criteria
Studies applying machine learning for obesity and overweight prediction

Exclusion Criteria
Studies not involving machine learning models for obesity prediction

Intervention/Exposure
Machine learning techniques, including supervised and unsupervised methods, for obesity and overweight prediction

Outcome Measures
Accuracy and effectiveness of ML models in predicting obesity and overweight

Funding Source
Not specified

Collaborating Institutions
University of Valladolid, European University of the Atlantic, Iberoamerican International University, University of Pinar del Río

Ethics Approval
Not specified

Publication Status
Published in Journal of Medical Systems (2023).

Keywords
Obesity, overweight, machine learning, prediction models, systematic review

Data Collection Methods
Systematic review and analysis of machine learning techniques

Primary Data Availability
Not applicable

Contact Information
Antonio Ferreras: This email address is being protected from spambots. You need JavaScript enabled to view it.