Automatic Classification of High vs. Low Individual Nutrition Literacy Levels from Loyalty Card Data in Switzerland

The increasingly prevalent diet-related non-communicable diseases (NCDs) constitute a modern health pandemic. Higher nutrition literacy (NL) correlates with healthier diets, which in turn has favorable effects on NCDs. Assessing and classifying people's NL is helpful in tailoring the level of education required for disease self-management/empowerment and adequate treatment strategy selection. With recently introduced regulation in the European Union and beyond, it has become easier to leverage loyalty card data and enrich it with nutrition information about bought products. We present a novel system that utilizes such data to classify individuals into high- and low- NL classes, using well-known machine learning (ML) models, thereby permitting for instance better targeting of educational measures to support the population-level management of NCDs. An online survey (n = 779) was conducted to assess individual NL levels and divide participants into high- and low- NL groups. Our results show that there are significant differences in NL between male and female, as well as between overweight and non-overweight individuals. No significant differences were found for other demographic parameters that were investigated. Next, the loyalty card data of participants (n = 11) was collected from two leading Swiss retailers with the consent of participants and a ML system was trained to predict high or low NL for these individuals. Our best ML model, which utilizes the XGBoost algorithm and monthly aggregated baskets, achieved a Macro-F1-score of .89 at classifying NL. We hence show the feasibility of identifying individual NL levels based on household loyalty card data leveraging ML models, however due to the small sample size, the results need to be further verified with a larger sample size.