Vol. 3 No. 2 (2023): Journal of Machine Learning for Healthcare Decision Support
Articles

A Comprehensive Analysis of AI-Driven Metabolomics for Precision Nutrition: Integrating Big Data and AI in Industry 4.0 for Tailoring Dietary Recommendations based on Individual Health Profiles

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  • Dr. Arjun Kapoor
Dr. Arjun Kapoor
Associate Professor, Literature Department, Azim Premji University, India
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Published 09-12-2023

Keywords

  • metabolomics,
  • precision nutrition,
  • Industry 4.0,
  • big data analytics,
  • dietary recommendations,
  • personalized healthcare
    • ...More
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Copyright (c) 2023 Dr. Arjun Kapoor (Author)

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
D. A. Kapoor, “A Comprehensive Analysis of AI-Driven Metabolomics for Precision Nutrition: Integrating Big Data and AI in Industry 4.0 for Tailoring Dietary Recommendations based on Individual Health Profiles”, Journal of Machine Learning for Healthcare Decision Support, vol. 3, no. 2, pp. 14–23, Dec. 2023, Accessed: Jan. 22, 2025. [Online]. Available: https://medlines.uk/index.php/JMLHDS/article/view/12

Abstract

This paper delves into the convergence of big data analytics and artificial intelligence (AI) within the realm of precision nutrition, specifically targeting the utilization of metabolomics data for tailoring dietary recommendations on an individual basis. As Industry 4.0 transforms the landscape of various sectors, including healthcare and nutrition, the integration of advanced technologies becomes paramount for enhancing personalization and efficacy. Metabolomics, as a discipline, offers profound insights into the biochemical processes within organisms, providing a holistic view of an individual's metabolic profile. Leveraging AI-driven metabolomics, this research endeavors to elucidate the intricate relationship between dietary interventions and metabolic responses, paving the way for precise and tailored nutritional recommendations. Through a comprehensive analysis, this paper elucidates the methodologies, challenges, and potential applications of AI-driven metabolomics in precision nutrition, thereby highlighting its significance in advancing healthcare practices in the era of Industry 4.0.

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References

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