Exploring Dietary Intake Data collected by FPQ using Unsupervised Learning

Populations in countries undergoing rapid transition are experiencing food- and nutrition-related problems. To acquire high-quality nutrition information, we need beside adequate data about food consumption, also efficient methods for the extraction of information from the collected data. Our aim was to develop a methodology for analyzing and reasoning about dietary intake data collected by a food propensity questionnaire (FPQ) and dependent 24-hour recalls (24HRs). 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.