Exploring Dietary Intake Data collected by FPQ using Unsupervised Learning
Authors
M. Gjoreski, S. Kochev, N. Reščič, M. Gregorič, T. Eftimov, B. Koroušić Seljak
Publication
IEEE BigData 2019 BFNDMA 2019 at IEEE BigData 2019
Los Angeles, USA, 8-12 December, 2019
Abstract
We analysed a subset of data (about 197 participants) in the SI.Menu survey carried out in 2016/17 in Slovenia. The participants completed FPQs and 24HRs. We were able to identify four clusters. Two clusters represented participants with more healthy habits, e.g., low intake of animal fats, high breakfast frequency, and high intake of fruits and vegetables. The other two clusters represented participants with less healthy habits, e.g., high intake of animal fats, low breakfast frequency and increased BMI. The four clusters can be well separated by only four variables. This interesting discovery could lead to simplified FFQ questionnaires, which could significantly decrease the participants' burden and could ensure participant compliance in similar studies. Having big national data set related to nutrition should ease the process of creating sustainable policies that will ultimately benefit agriculture, human health and the environment.
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