Background/Objectives: Chronic undernutrition in the first 1000 days continues to be a significant public health concern in Ecuador. This study examined associations between infant feeding practices, maternal supplementation, socioeconomic factors, and stunting in the first two years of life using explainable machine-learning methods. Methods: The analysis was based on 8613 eligible children aged 0–23 months from the Ecuadorian National Child Malnutrition Survey (Encuesta Nacional sobre Desnutrición Infantil, ENDI) 2023–2024, of whom 8344 had non-missing stunting status and were included in the final modelling sample. Results: Among the children included in the modelling sample, stunting was observed in 18.7%. Maternal iron and folic-acid supplementation were associated with lower stunting prevalence, while breastfeeding and complementary feeding indicators showed weak linear associations but contributed to model predictions in explainability analyses. SHapley Additive exPlanations (SHAP) additionally highlighted geographic and socioeconomic variation, with province, area of residence, and socioeconomic indicators ranking among the features contributing to model prediction. Predictive performance was modest to moderate across models, with area under the receiver operating characteristic curve (AUC) values ranging approximately from 0.62 to 0.66 across the main model specifications. Model performance remained stronger for the majority non-stunted class than for the minority stunted class, highlighting the challenge of detecting stunted children in an imbalanced analytic ENDI sample. Conclusions: Overall, the results suggest that stunting in the analytic sample was represented by multidimensional predictive patterns involving prenatal, nutritional, socioeconomic, and geographic factors, and illustrate the potential value of explainable machine learning for identifying multidimensional risk patterns in child nutrition research.

Public attitudes toward immigration in Spain are influenced by media narratives, individual traits, and emotional responses. This study examines how portrayals of Arab and African immigrants may be associated with emotional and attitudinal variation. We address three questions: (1) How are different types of media coverage and social environments linked to emotional reactions? (2) What emotions are most frequently associated with these portrayals? and (3) How do political orientation and media exposure relate to changes in perception? A pre/post media exposure survey was conducted with 130 Spanish university students. Machine learning models (decision tree, random forest, and support vector machine) were used to classify attitudes and identify predictive features. Emotional variables such as fear and happiness, as well as perceptions of media clarity and bias, emerged as key features in classification models. Political orientation and prior media experience were also linked to variation in responses. These findings suggest that emotional and contextual factors may be relevant in understanding public perceptions of immigration. The use of interpretable models contributes to a nuanced analysis of media influence and highlights the value of transparent computational approaches in migration research.