Artificial Intelligence–Driven Models for Predicting Adolescent Obesity: A Comprehensive Review
DOI:
https://doi.org/10.56450/JEFI.2025.v3i03.002Keywords:
Artificial Intelligence, Machine Learning, Predictive Modeling, Risk Stratification, Deep Learning; Childhood and Adolescent Obesity, Public Health InformaticsAbstract
Childhood and adolescent obesity is an escalating global public health concern, driven by complex interactions among biological, behavioral, environmental, and social determinants. Conventional statistical approaches often fail to capture these nonlinear and high-dimensional relationships. Artificial intelligence (AI), particularly machine learning (ML), offers robust tools for early risk prediction and stratification. This review synthesizes evidence on AI- and ML-driven approaches for predicting obesity among children and adolescents. A structured literature search was conducted between February and March 2025 across PubMed, Scopus, Web of Science, IEEE Xplore, ScienceDirect, and Google Scholar. Studies focusing on AI-based prediction of obesity in populations aged ≤19 years were included, while studies limited to adults or lacking methodological transparency were excluded. Evidence from 2015–2025 demonstrates that supervised ML models—especially random forests, gradient boosting, and deep learning architectures—achieve high predictive performance when applied to electronic health records, cohort data, and lifestyle datasets. However, gaps remain in model explainability, multimodal data integration, representation from low- and middle-income countries, and real-world implementation. Addressing these limitations is critical for translating AI-based obesity prediction into effective prevention and clinical decision-support systems.
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References
1. WHO. “Obesity and Overweight.” World Health Organization, WHO, 1 Mar. 2024, www.who.int/news-room/fact-sheets/detail/obesity-and-overweight.
2. GBD 2021 Adolescent BMI Collaborators. Global, regional, and national prevalence of child and adolescent overweight and obesity, 1990–2021, with forecasts to 2050: a forecasting study for the Global Burden of Disease Study 2021. The Lancet. 03 March 2025, doi: 10.1016/S0140-6736(25)00397-6.
3. Colmenarejo, Gonzalo. “Machine Learning Models to Predict Childhood and Adolescent Obesity: A Review.” Nutrients, vol. 12, no. 8, 16 Aug. 2020, p. 2466, https://doi.org/10.3390/nu12082466.
4. Meharunnissa Khaskheli, et al. “Menstrual Irregularities, Hormonal Imbalance and Obesity in Adolescent Girls in Hyderabad, Sindh, Pakistan: An Observational Study.” Journal of Health Research, vol. 37, no. 1, 4 Aug. 2022, pp. 26–32,
5. Kumar S, Mahapatra T, Dasgupta R, Soan V, Sahoo SK, Mahapatra S, et al. Urbanization and childhood obesity in India: a cross-sectional study among adolescents in six urbanizing villages. PLoS One. 2020;15(6):e0234570.
6. Gupta M, Phan T-LT, Bunnell T, Beheshti R. Obesity prediction with EHR data: a deep learning approach with interpretable elements. arXiv. 2019 Dec 5. Report No.: arXiv:1912.02655. Available from: https://arxiv.org/abs/1912.02655
7. J. -H. Jeong, I. -G. Lee, S. -K. Kim, T. -E. Kam, S. -W. Lee and E. Lee, "DeepHealthNet: Adolescent Obesity Prediction System Based on a Deep Learning Framework," in IEEE Journal of Biomedical and Health Informatics, vol. 28, no. 4, pp. 2282-2293, April 2024, doi: 10.1109/JBHI.2024.3356580.
8. Hammond R, Athanasiadou R, Curado S, Aphinyanaphongs Y, Abrams C, Messito MJ, Gross R, Katzow M, Jay M, Razavian N, Elbel B. Predicting childhood obesity using electronic health records and publicly available data. PLoS One. 2019 Apr 22;14(4):e0215571.
9. Pang J, Liu Q, Zhao X. Pediatric obesity prediction using large-scale electronic health data. BMC Medical Informatics and Decision Making. 2023;23(1):1-14.
10. Kim, J., Choi, Y., & Lee, K. (2019). Predicting Factors Affecting Adolescent Obesity Using General Bayesian Network and What-If Analysis. International Journal of Environmental Research and Public Health, 16(23), 4684.
11. Öksüz İ, Demirci M, Aydın B. Comparison of machine learning algorithms for obesity risk prediction. Journal of Health Informatics. 2023;14(2):1-10.
12. Zheng Y, Ruggiero L. Childhood obesity prediction using ensemble learning models. In: 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM); 2017 Nov 13-16; Kansas City, MO. Piscataway (NJ): IEEE; 2017:817-822.
13. Kaur G, Sharma P, Gupta R. Prediction of adolescent obesity using hybrid machine learning models: a comparative analysis. In: 2023 International Conference on Computer, Communication, and Information Sciences (CCCIS); 2023 Feb 17-18; Uttar Pradesh, India. New York: IEEE; 2023:1–6.
14. Wang L, Li X. A machine learning framework for early detection of adolescent obesity. In: 2022 IEEE 2nd International Conference on Consumer Electronics and Computer Engineering (ICCECE); 2022 Dec 2-4; Wuhan, China. Piscataway (NJ): IEEE; 2022:433-437.
15. Sulistya RYI, Istighosah M. Obesity prediction with machine learning models comparing various algorithm performances. International Journal of Artificial Intelligence in Medical Issues. 2025;5(2):12-21.
16. Huang W, Zhang J, Li S. YOLOv8-based body composition analysis for adolescent obesity detection. BMC Public Health. 2023;24(1):1-12..
17. Awari S, Kaushik M. DeepHealthNet: Adolescent Obesity Prediction System Based on a Deep Learning Framework. Zenodo. 2024. Available from: https://zenodo.org/records/15254162.
18. Jeong J-H, Lee I-G, Kim S-K, Kam T-E, Lee S-W, Lee E. DeepHealthNet: adolescent obesity prediction system based on a deep learning framework. arXiv. 2023 Aug 28. Report No.: arXiv:2308.14657. Available from: https://arxiv.org/abs/2308.14657
19. Allen N, Smith K, Johnson M. Adolescent obesity risk prediction using ABCD study data. J Adolesc Health. 2024;15(3):123-130.
20. Abdallah M, Kamaruddin N, Mohamed N, et al. Comparison of Bayesian networks, decision trees, naïve Bayes, artificial neural networks, and support vector machines for predicting obesity at age 12 years in a Malaysian cohort. J Med Syst. 2017;41(10):155
21. Dugan TM, Mukhopadhyay S, Carroll A, Downs SM. Machine learning techniques for prediction of early childhood obesity. Appl Clin Inform. 2015;6(3):506-520.
22. Turer CB, Barlow SE, Montaño S, Hoang KC, Flores G. Predicting risk of childhood obesity at age 5 years using electronic health records. Pediatrics. 2018;142(2):e20174004.
23. Crouch, Elizabeth. “Rural-Urban Differences in Overweight and Obesity, Physical Activity, and Food Security among Children and Adolescents.” Preventing Chronic Disease, vol. 20, no. 20, 19 Oct. 2023,
24. Gong, H., Zhao, Y. Association between body roundness index and sleep disorder: the mediating role of depression. BMC Psychiatry 25, 212 (2025). https://doi.org/10.1186/s12888-025-06664-z
25. Fismen, AS., Aarø, L.E., Thorsteinsson, E. et al. Associations between eating habits and mental health among adolescents in five Nordic countries: a cross-sectional survey. BMC Public Health 24, 2640 (2024). https://doi.org/10.1186/s12889-024-20084-w
26. Lobstein T, Brinsden H. The Child Obesity Pandemic: Promoting Social Change Through Public Health Policy. *Pediatr Obes*. 2022;17(5):e12933. doi:10.1111/ijpo.12933.
27. Kelly AS, Auerbach P, Barrientos-Perez M, et al. Severe Obesity in Children and Adolescents: Challenges and Opportunities for Treatment. *Obesity*. 2023;31(1):5–17.
28. Yu E, Ley SH, Bhupathiraju SN, et al. Risk factors and consequences of childhood obesity: Global insights and mitigation strategies. *Lancet Child Adolesc Health*. 2022;6(8):543–554.
29. February2025 Citation: Azmi,S.;Kunnathodi,F.; Alotaibi, H.F.; Alhazzani, W.; Mustafa, M.; Ahmad,I.;Anvarbatcha,R.;Lytras, M.D.; Arafat, A.A.Harnessing Artificial Intelligence in Obesity Research andManagement: A ComprehensiveReview. Diagnostics 2025, 15, 396. https://doi.org/10.3390/ diagnostics15030396.
30. Ab Majid, Nur Liana, and Syahid Anuar Anuar. “Machine Learning Modelling for Imbalanced Dataset: Case Study of Adolescent Obesity in Malaysia.” Semarakilmu.com, Journal of Advanced Research in AppliedSciencesand Engineering Technology, 25 Dec. 2023, semarakilmu.com.my/journals/index.php/applied_sciences_eng_tech/index.
31. Han, Jiawei, and Micheline Kamber. Data Mining : Concepts and Techniques. Amsterdam ; Boston, Elsevier/Morgan Kaufmann, 2012.
32. H. Siddiqui et al., "A Survey on Machine and Deep Learning Models for Childhood and Adolescent Obesity," in IEEE Access, vol. 9, pp. 157337-157360, 2021, doi: 10.1109/ACCESS.2021.3131128.
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Copyright (c) 2025 Rakshitha J, Sunil Kumar D, Arun Vanishri, Santhosh Kumar M, Krishnamurthy KV (Author)
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