Factors Associated with Nutritional Status in Grassroots Recyclers in Ecuador: A Machine Learning Approach.

Grassroots recyclers play a fundamental role in solid waste management in Ecuador; however, they often work under precarious conditions that may compromise their health. This study aimed to identify factors associated with nutritional status, operationalized as the presence or absence of nutritional alterations, among grassroots recyclers through supervised machine learning approaches. Data from 303 recyclers from three Ecuadorian cities (Cuenca, Macas, and La Libertad) were analyzed, incorporating sociodemographic, occupational, and health-related variables. Nutritional alterations were defined based on anthropometric and biochemical indicators, specifically, excess body weight and/or elevated total lipid levels. The results showed that 71% presented nutritional alterations, evidencing an important public health problem in this vulnerable population. Significant associations were observed with sex, age, canton of residence, ability to ride a bicycle, bicycle use for work, and attendance at medical check-ups. Among the evaluated models, CatBoost trained with SMOTE achieved the highest ROC-AUC value and the most balanced performance between classes, although sensitivity for individuals without nutritional alterations remained limited. Feature importance analysis highlighted sociodemographic, occupational, economic, and healthcare access factors, underscoring the multidimensional nature of nutritional risk and supporting the use of machine learning as a support tool for public health planning and targeted interventions.