Explainable Machine Learning Models for Identification of Food-Related Lifestyle Factors in Chicken Meat Consumption Case in Northern Greece

Abstract

A consumer’s decision-making process regarding the purchase of chicken meat is a multifaceted one, influenced by various food-related, personal, and environmental factors that interact with one another. The mediating effect of food lifestyle that bridges the gap between consumer food values and the environment, further shapes consumer behavior towards meat purchase and consumption. This research introduces the concept of Food-Related Lifestyle (FRL) and aims to identify and explain the factors associated with chicken meat consumption in Northern Greece using a machine learning pipeline. To achieve this, the Boruta algorithm and four widely recognized classifiers were employed, achieving a binary classification accuracy of up to 78.26%. The study primarily focuses on determining the items from the FRL tool that carry significant weight in the classification output, thereby providing valuable insights. Additionally, the research aims to interpret the significance of these selected factors in the decision-making process using the SHAP model. Specifically, it turns out that the freshness, safety, and nutritional value of chicken meat are essential considerations for consumers in their eating habits. Additionally, external factors like health crises and price fluctuations can have a significant impact on consumer choices related to chicken meat consumption. The findings contribute to a more nuanced understanding of consumer preferences, enabling the food industry to align its offerings and marketing efforts with consumer needs and desires. Ultimately, this work demonstrates the potential of AI in shaping the future of the food industry and informs strategies for effective decision-making.