AI RESEARCH
An Explainable Unsupervised-to-Supervised Machine Learning Framework for Dietary Pattern Discovery Using UK National Dietary Survey Data
arXiv CS.AI
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ArXi:2605.08242v1 Announce Type: cross Clinical dietary assessment can generate detailed but high-dimensional nutrient and food-group information that is difficult to translate quickly into counselling priorities. This paper proposes an explainable unsupervised-to-supervised machine learning framework for discovering, reproducing and interpreting dietary patterns using public UK National Diet and Nutrition Survey data. Adult participants aged 19 years and above from NDNS Years 12-15 were represented using 25 energy-adjusted nutrient and food-group features.