A machine learning (ML) approach to understanding participation in government nutrition programs

Machine Learning (ML) affords researchers tools to advance beyond research methods commonly employed in psychology, business, and public policy studies of federal nutrition programs and participant food decision-making. It is a sub domain of AI that is applied for feature extraction – a crucial step in decision making. These features are used in context-specific automated decisions resulting in predictive AI models. Whereas many prior studies rely on retrospective, static, “one-shot” decision-making in controlled laboratory environments, ML allows researchers to refine predictions about participation and food behaviors using large-scale datasets. We propose a case study using ML to predict an aspect of participation in a large, publicly funded nutrition education program (The Expanded Food and Nutrition Education Program). Participation has important downstream implications for diet quality, food security, and other important nutrition related decisions. We then suggest a process for validating the ML insights using qualitative research and survey data.