Bayesian Network analysis of spatial disparities in child nutrition status and health determinants across Indonesia

• The research employs Bayesian Network and spatial analysis to pinpoint critical factors contributing to child malnutrition in Indonesia. • Exclusive breastfeeding, immunization, antenatal care, and growth monitoring are essential determinants affecting nutritional outcomes. • High-risk groups of malnutrition are found, which shows where targeted interventions are needed. • The model evaluation demonstrates robust predictive performance for various variables, although certain indicators necessitate supplementary contextual data. • Findings support evidence-based policies that are in line with Indonesia’s National Strategy for Stunting Reduction to improve the nutrition of children all over the country. Child malnutrition continues to be a significant public health issue in Indonesia, with considerable spatial variations among provinces. This study utilizes an integrated framework that combines Bayesian Network (BN) modeling, Local Indicators of Spatial Association (LISA), and Moran’s I analysis to ascertain the principal determinants of child nutritional status and their spatial distribution. The Bayesian Network (BN) probabilistic approach captures intricate interdependencies among health and behavioral factors, such as Correct Knowledge about Stunting (CKS), Exclusive Breastfeeding (EBF), Breastfeeding for Two Years (BF2Y), Complementary Feeding (CF), Immunization (IMM), Growth Monitoring (GM), Iron-Folic Acid Supplementation (IFA), Antenatal Care (ANC6), and Animal Protein Intake for Pregnant Women (APH), and their impact on outcomes classified as Severely Underweight (SUW), Underweight (UW), and Normal (N). Spatial statistical analyses indicate considerable heterogeneity and elevated-risk clusters of malnutrition, underscoring areas necessitating prioritized interventions. Stochastic simulations elucidate the variability and robustness of critical determinants, whereas model evaluation via Mean Squared Error (MSE) indicates robust predictive performance for variables such as N, ANC6, and SUW, with heightened uncertainty noted for EBF, IFA, and IMM, implying the impact of supplementary social or contextual factors. This study combines probabilistic inference with spatial analysis to show the pathways and interactions that lead to child malnutrition. This gives useful information for focused, evidence-based interventions. The results support strategies that involve more than one sector and are in line with Indonesia’s National Strategy for Stunting Reduction (Presidential Regulation No. 72 of 2021). This will help resources be used more effectively, policies be planned better, and children’s nutrition outcomes be more fair across the country.