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Artificial Intelligence in Health Synthetic data for obesity level prediction

Acknowledgments 6. Shi R, Wang Y, Du M, Shen X, Wang X. A Comprehensive
Survey of Synthetic Tabular Data Generation. [arXiv
None. Preprint]; 2025.
Funding doi: 10.48550/arXiv.2504.16506

None. 7. Hernadez M, Epelde G, Alberdi A, Cilla R, Rankin D.
Synthetic tabular data evaluation in the health domain
Conflict of interest covering resemblance, utility, and privacy dimensions.
Methods Inf Med. 2023;62(S01):e19-e38.
The authors declare that they have no competing interests.
doi: 10.1055/s-0042-1760247
Author contributions 8. Arora A, Arora A. Generative adversarial networks and
synthetic patient data: Current challenges and future
Conceptualization: All authors perspectives. Fut Healthc J. 2022;9(2):190-193.
Formal analysis: All authors
Investigation: All authors doi: 10.7861/fhj.2022-0013
Methodology: Hakan Alp Eren, Sinem Bozkurt Keser 9. Sámano R, Lopezmalo-Casares S, Martínez-Rojano H, et al.
Writing – original draft: All authors Early life determinants of overweight and obesity in a sample
Writing – review & editing: All authors of Mexico city preschoolers. Nutrients. 2025;17(4):697.
doi: 10.3390/nu17040697
Ethics approval and consent to participate
10. Sobas K, Suliga E, Bryk P, Gluszek S. Dietary patterns and
Not applicable. nutritional status in bariatric surgery candidates-a cross-
sectional study. Nutrients. 2025;17(4):716.
Consent for publication
doi: 10.3390/nu17040716
Not applicable. 11. Colonnello E, Libotte F, Masi D, et al. Eating behavior

Availability of data patterns, metabolic parameters and circulating oxytocin
levels in patients with obesity: An exploratory study. Eating
The dataset used in this study is publicly available from the Weight Disord. 2025;30(1):6.
University of California, Irvine ML Repository under the doi: 10.1007/s40519-024-01698-w
title Estimation of Obesity Levels Based on Eating Habits and 12. El-Sehrawy AAMA, Khachatryan LG, Kubaev A, et al.
Physical Condition: https://archive.ics.uci.edu/dataset/544. Triglyceride-glucose index: A potent predictor of metabolic
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