Page 79 - AIH-2-4

Artificial Intelligence in Health Synthetic data for obesity level prediction

cities. Bioengineering (Basel). 2024;11(6):533. 28. Ganie SM, Reddy BB, Rege M. An investigation of ensemble
learning techniques for obesity risk prediction using lifestyle
doi: 10.3390/bioengineering11060533
data. Decis Analyt J. 2025;14:100539.
17. Shakti MAS, Vijayalakshmi M, Kumar N, Vaidhehi M.
Analysis on Various Machine Learning Framework doi: 10.1016/j.dajour.2024.100539
for Obesity Level Prediction. In: Proceedings of the 29. Nagarajan SG, Balasubramanian V, Gonugunta P, Gudla SK.
1 International Conference on Contemporary Global Obesity level prediction using deep learning approach-a
st
Challenges and Urban Innovations (ICCGUI) IEEE. Vol. 1; comparative analysis. Eng Appl Sci Res. 2024;51(4):540-554.
2024. p. 406-411.
30. Umoh PN, Nneji GU, Monday HN, et al. Optimizing
doi: 10.1109/IC-CGU58078.2024.10530812 machine learning classifiers and feature selection techniques
18. Yağmur N. A hybrid approach to obesity level determination for obesity levels estimation using physical habits and
with decision tree and pelican optimization algorithm. J Sci dietary data. World Sci News. 2024;198:326-353.
Rep A. 2024;57:97-109. doi: 10.1142/WSN198(2024)325-353
doi: 10.59313/jsr-a.1447814 31. Vairachilai S, Periyanayagi S, Raja SPR. PIPR machine
19. Özkurt C. Examination and evaluation of obesity risk factors learning model: Obesity impact analysis. Open Biomed Eng
with explainable artificial intelligence. Comput Electron J. 2024;18(1):1-20.
Med. 2024;1(1):12-17. doi: 10.2174/0118741207289421240430115207
doi: 10.69882/adba.cem.2024072 32. Forte P, Encarnação S, Monteiro AM, et al. A deep learning
20. Wang X. Predicting obesity risk through lifestyle habits: neural network to classify obesity risk in portuguese
A comparative analysis of machine learning models. E3S adolescents based on physical fitness levels and body mass
Web Conf. 2024;385:05037. index percentiles: Insights for national health policies. Behav
Sci. 2023;13(7):522.
doi: 10.1051/e3sconf/202455305037
doi: 10.3390/bs13070522
21. Okpe OA, Odey JA, Abiodum OJ. A novel multi-class
classification of obesity level using artificial neural network. 33. Yağın FH, Gülü M, Görmez Y, et al. Estimation of obesity
Int J Adv Multidiscip Res Studies. 2024;4(3):1374-1379. levels with a trained neural network approach optimized by
the Bayesian technique. Appl Sci. 2023;13(6):3875.
22. Azad M, Khan MFK, El-Ghany SA. XAI-enhanced machine
learning for obesity risk classification: A stacking approach doi: 10.3390/app13063875
with LIME explanations. IEEE Access. 2025;13:13847-13865. 34. Gözükara Bağ HG, Yağın FH, Görmez Y, et al. Estimation
doi: 10.1109/ACCESS.2025.3530840 of obesity levels through the proposed predictive approach
based on physical activity and nutritional habits. Diagnostics.
23. Solomon DD, Khan S, Garg S, et al. Hybrid majority voting: 2023;13(18):2949.
Prediction and classification model for obesity. Diagnostics
(Basel). 2023;13(15):2610. doi: 10.3390/diagnostics13182949
doi: 10.3390/diagnostics13152610 35. Yang Y, Khorshidi HA, Aickelin U. A review on over-
sampling techniques in classification of multi-class
24. Kaur R, Kumar R, Gupta M. Predicting risk of obesity and imbalanced datasets: Insights for medical problems. Front
meal planning to reduce obesity in adulthood using artificial Digit Health. 2024;6:1430245.
intelligence. Endocrine. 2022;78(3):458-469.
doi: 10.3389/fdgth.2024.1430245
doi: 10.1007/s12020-022-03215-4
36. Lemaître G, Nogueira F, Aridas CK. Imbalanced-learn:
25. Muliawan A, Fauziah DA, Afrianto E. Obesity risk prediction A python toolbox to tackle the curse of imbalanced datasets
using random forest based on eating habit parameters. in machine learning. J Mach Learn Res. 2017;18(17):1-5.
INSIDE J. 2024;2(1):13-18.
37. Xu L, Skoularidou M, Cuesta-Infante A, Veeramachaneni
26. Choudhuri A. A Hybrid Machine Learning Model for K. Modeling Tabular Data using Conditional GAN. In:
Estimation of Obesity Levels. In: Proceedings of the Advances in Neural Information Processing Systems; 2019.
International Conference on Data Management, Analytics p. 32. Available from: https://proceedings.neurips.cc/
and Innovation. Vol. 137; 2023. p. 414-423. paper/2019/hash/254ed7d2de3b23ab10936522dd547b78-
doi: 10.1007/978-981-19-2600-6_22 abstract.html [Last accessed on 2024 Dec 12].
27. Cervantes RC, Palacio ALH. Estimation of obesity levels 38. Patki N, Wedge R, Veeramachaneni K. The Synthetic Data
based on computational intelligence. Inf Med Unlocked. Vault. In: International Conference on Data Science and
2020;21:100472. Advanced Analytics (DSAA); 2016. p. 399-410.
doi: 10.1016/j.imu.2020.100472 doi: 10.1109/DSAA.2016.49

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