ARCHIVES
VOL. 8, ISSUE 1 (2026)
Artificial intelligence in predicting obesity risk and designing preventive nutrition therapies: A review
Authors
Purva Gopal Sharma, Alpana Joshi, Anmayee Nanda, Shaikh Shahid, Souvik Tewari, Somnath Das
Abstract
Obesity has emerged as one of the most
pressing global public health challenges, driven by complex interactions among
genetic, behavioural, environmental, and socioeconomic factors. Conventional
approaches to obesity risk assessment and nutritional intervention often rely
on population-based models that fail to account for individual variability.
Recent advances in artificial intelligence (AI) and machine learning (ML) have
transformed obesity research by enabling accurate prediction of obesity risk
and facilitating the development of personalized, preventive nutrition
therapies. This review explores the role of AI in obesity prediction, including
the use of machine learning algorithms, deep learning models, and big data
analytics to integrate anthropometric, dietary, lifestyle, genetic, and
metabolic data. Furthermore, it highlights AI-driven approaches for designing
personalized nutrition interventions, behaviour modification strategies, and
digital health tools for obesity prevention. Ethical challenges, data
limitations, and future research directions are also discussed. The review
underscores the potential of AI-based systems to revolutionize preventive
nutrition and support precision public health strategies for obesity
management.
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Pages:7-11
How to cite this article:
Purva Gopal Sharma, Alpana Joshi, Anmayee Nanda, Shaikh Shahid, Souvik Tewari, Somnath Das "Artificial intelligence in predicting obesity risk and designing preventive nutrition therapies: A review". International Journal of Medical Science and Clinical Research, Vol 8, Issue 1, 2026, Pages 7-11
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