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Development of an AI-Driven Computational Framework for Integrated Dietary Pattern Assessment: A Simulation-Based

Mohammad Fazle Rabbi1

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This study introduces an AI framework to assess diets for nutrition, environmental impact, and cost. It shows AI can optimize food choices for sustainability and health.

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artificial intelligencedietary patternsenvironmental footprintmachine learningnutrient adequacy

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Area of Science:

  • Computational nutrition and environmental sustainability science.
  • Development of artificial intelligence (AI) models for dietary assessment.
  • Interdisciplinary research integrating food systems, public health, and environmental science.

Background:

  • Conventional dietary assessments fail to integrate nutritional adequacy with environmental sustainability and economic factors.
  • Food systems require methodologies that balance nutritional needs with resource conservation.
  • A gap exists in computational tools for holistic dietary pattern evaluation.

Purpose of the Study:

  • To develop and validate an AI-driven computational framework for integrated dietary assessment.
  • To synthesize nutritional evaluation, environmental footprint quantification, and economic accessibility.
  • To benchmark dietary patterns against sustainability thresholds and epidemiological data.

Main Methods:

  • Simulation-based proof-of-concept study using a cohort of 1500 individuals.
  • Integration of random forest classification, dimensionality reduction, and scenario-based optimization.
  • Analysis of 55 foods across eight categories, incorporating greenhouse gas emissions, water use, and price data.

Main Results:

  • AI framework achieved 39.1% classification accuracy, with cost, emissions, and water use as key discriminators.
  • Dietary patterns showed similar macronutrient profiles but widespread calcium inadequacy.
  • Scenario modeling identified flexible diets reducing water use by 13% with minimal cost increases.
  • AI-driven assessment highlighted substantial energy intake deviations when prioritizing nutritional adequacy.

Conclusions:

  • The developed AI framework provides a validated computational infrastructure for integrated dietary assessment.
  • Demonstrates the feasibility of AI in evaluating dietary patterns across nutritional, environmental, and economic dimensions.
  • Highlights the potential for optimizing food systems towards sustainability and improved public health outcomes.