An Explainable Artificial Intelligence Framework for Chronic Malnutrition Surveillance in a National Health Information System: A CatBoost-SHAP Approach in Peru

Navarro-Guillén E.; Yauri J.; Escalante-Pablo L.; Carrillo-Riveros E.; Guevara-Morote E.; Carreño-Gamarra J.C.; Luján-Pérez D. · Ingenierie des Systemes d'Information (2026) · DOI: 10.18280/isi.310716

Resumen

Childhood chronic malnutrition remains a major public health concern, particularly in low- and middle-income regions where timely identification of vulnerable populations is essential for effective intervention. However, the deployment of predictive models in national health information systems is often limited by scalability, heterogeneous data structures, and insufficient interpretability. This study proposes an explainable artificial intelligence (XAI) framework for chronic malnutrition surveillance using the Peruvian Nutritional Status Information System (SIEN). A CatBoost-based classification model was developed using 1,794,809 national health records collected in 2024, incorporating anthropometric, demographic, and socioeconomic variables. The model was designed to handle high-dimensional categorical information without manual encoding while maintaining predictive transparency through Shapley Additive Explanations (SHAP) analysis. A stratified 10-fold cross-validation strategy was applied to evaluate model robustness. The proposed framework achieved an accuracy of 99.44%, recall of 97.62%, precision of 98.84%, F1-score of 98.22%, specificity of 99.78%, and area under the receiver operating characteristic curve (ROC-AUC) of 99.86%. SHAP analysis revealed that anthropometric indicators, particularly Height_for_Age, were the dominant predictors, while social program variables showed comparatively limited contribution within the predictive model. Beyond predictive performance, this work presents a deployment-oriented architecture integrating data processing, inference services, and interpretability mechanisms for national health information environments. The proposed framework provides a scalable approach for supporting evidence-based nutritional surveillance and early identification of children at risk of chronic malnutrition in Peru.

Instituciones: Universidad Nacional de San Cristóbal de Huamanga