Thesis subject

Artificial Intelligence for optimizing nutritional strategies in aging populations (MSc)

As global demographics shift towards older populations, the importance of optimizing nutrition for healthy aging and the prevention of age-related diseases becomes increasingly evident. Proper nutrition plays a vital role in maintaining health, reducing the risk of chronic conditions, and enhancing the quality of life for older adults. Recent advancements in artificial intelligence provide innovative tools to analyze and interpret vast amounts of data related to nutrition and health.

Short description

As global demographics shift towards older populations, optimizing nutrition becomes crucial for promoting healthy aging and preventing age-related diseases. Artificial Intelligence offers innovative tools to analyze vast amounts of open access data on nutrition, health outcomes, and aging, providing insights to tailor dietary recommendations effectively. This MSc project aims to use AI techniques to develop personalized nutritional strategies that enhance health and longevity in older adults, utilizing publicly available data sources.


Objectives

  1. Conduct a comprehensive literature review on AI applications in nutrition, health, and aging, focusing on studies using open access data.
  2. Develop AI algorithms to analyze open access datasets and identify correlations between dietary patterns and health outcomes in aging populations.
  3. Identify key nutritional factors that influence healthy aging and their relative impact using AI-driven data analysis.
  4. Create a personalized nutritional recommendation system based on individual characteristics and health goals derived from open access data.
  5. Provide insights and recommendations for implementing AI-driven nutritional strategies to optimize aging outcomes in clinical and community settings.

    Tasks

    The work in this master thesis entails:

    • Literature review:Conduct a thorough review of existing research on AI applications in nutrition, health, and aging, specifically examining studies utilizing open access data.
    • Data collection and preparation:Identify and collect relevant open access data sources, including nutritional databases (e.g., USDA databases), health surveys (e.g., NHANES), and aging-related datasets (e.g., WHO reports).
    • AI Algorithms development:Develop and train AI algorithms to analyze relationships between dietary factors and aging outcomes using open access data.
    • Personalized nutritional recommendation system:Design a system that leverages AI analysis to generate personalized dietary recommendations tailored to individual health profiles and aging concerns.
    • Results reporting and documentation:Prepare a comprehensive report detailing the research methodology, AI model development, results, and conclusions.


    Literature

    • Kirk, Daniel, Cagatay Catal, and Bedir Tekinerdogan. "Precision nutrition: A systematic literature review."Computers in Biology and Medicine133 (2021): 104365.
    • Liu, Ningjing ; Bouzembrak, Yamine ; Bulk, Leonieke M. van den; Gavai, Anand ; Heuvel, Lukas J. van den; Marvin, Hans J.P. Automated food safety early warning system in the dairy supply chain using machine learning (2022) Food Control 136 .

    Requirements

    • Required skills/knowledge: Food and health, Machine Learning, Programming (Python/R).

      Key words: Artificial Intelligence, nutrition, aging, food.

      Contact person(s)

      Dr. Yamine Bouzembrak (yamine.bouzembrak@wur.nl)
      Prof. Bedir Tekinerdogan (bedir.tekinerdogan@wur.nl)