Machine Learning methods and models to predict food insecurity levels for families in Ceará, Brazil, based on employment, housing and other social indicators

Many nations still struggle to provide their populations with access to food and balanced nutrition. The Food and Agriculture Organization of the United Nations (FAO) included Brazil in its 2022 Hunger Map, highlighting that 61 million Brazilians face difficulties in feeding themselves. Despite the presence of various food security alert and monitoring systems in food-insecure countries, the data and methodologies they rely on capture only a fraction of the issue’s complexity, underscoring the need for further research to fully comprehend this multifaceted problem. In response, the Secretary for Social Protection of Ceará (SPS - Secretaria de Proteção Social), located in Brazil’s northeast, conducted a survey to collect data on the social and economic characteristics of extremely vulnerable families. This dataset, analyzed in our study, represents a concentrated effort by the government of Ceará to evaluate the needs of low-income households, particularly those with children who lack access to essential services. We used the Brazilian Food Insecurity Scale, a tool validated by the Brazilian Ministry, to measure food insecurity levels based on families’ responses, assigning scores to their answers. This paper presents a machine learning model that examines the collected data to identify which factors related to Food Access, Employment and Income, Housing, and Public Services can predict levels of food insecurity. Our best model demonstrates an accuracy of approximately 0.75, an F1-score of 0.80, and can distinguish between severe and non-severe food insecurity levels. We suggest that our model could be applied to other datasets lacking nutrition-specific questions to gauge a family’s food insecurity level. Additionally, our research sheds light on the key factors influencing food insecurity levels in Brazil, notably income and housing conditions, providing valuable insights for addressing this issue.