Analyzing the Impact of Organic Food Consumption on Citizens Health Using Unsupervised Machine Learning

Giulio Angiolini and Giovanna Maria Dimitri ()
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Giulio Angiolini: Dipartimento Ingegneria dell’Informazione e Scienze Matematiche (DIISM), Universitá di Siena, 53100 Siena, Italy
Giovanna Maria Dimitri: Dipartimento Ingegneria dell’Informazione e Scienze Matematiche (DIISM), Universitá di Siena, 53100 Siena, Italy

Mathematics, 2025, vol. 13, issue 8, 1-33

Abstract: Despite the growing popularity of organic foods, research on their effects on human health, particularly regarding cancer and diabetes, remains limited. While some studies suggest potential health benefits, others yield conflicting results or lack sufficient evidence to draw conclusions. Understanding the causal relationship between organic food consumption and health outcomes is challenging, especially with limited datasets. Our study examines the correlation between organic food consumption and the prevalence of cancer and diabetes in European nations over time. We compared these findings with data from 100 Italian citizens regarding their perceptions of organic food’s health benefits collected through a novel questionnaire. To identify patterns, we applied Affinity Propagation clustering to group countries based on organic food consumption and disease prevalence. We also created an animated map to visualize cluster progression over time and used the Global Multiplexity Index to evaluate consistency. Our analysis revealed two subgroups of European countries exhibiting significant similarities in organic food consumption and health outcomes. The clustering analysis performed year-by-year on three variables across European nations using the Affinity Propagation algorithm revealed that two clusters consistently maximized the Global Multiplexity Index over time. The first cluster included Belgium, Finland, Ireland, Italy, and Spain, while the second comprised Bulgaria, Turkey, Romania, Ukraine, Czech Republic, Hungary, Poland, Greece, and Russia. These clusters displayed distinct trends concerning sustainable development goals (SDGs) related to organic farming and non-communicable diseases. Additionally, mapping SDG indicators along with geographic and socio-economic factors supported our findings. Moreover, we introduced a novel dataset and offered insights into both the European context and the Italian scenario, contributing to further research on organic food’s impact on public health.

Keywords: unsupervised learning; data science; organic food (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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