<![CDATA[Childhood stunting remains a critical global health concern, reflecting chronic malnutrition that affects both physical growth and long-term cognitive development. Despite ongoing interventions, early detection in many low- and middle-income countries is still hindered by limited resources and the absence of interpretable decision-support tools. This study aims to develop and evaluate an explainable machine learning framework to predict stunting among toddlers using simple anthropometric and demographic data, thereby supporting evidence-based public health interventions. Data were collected from 40,071 children aged 0–59 months in Jeneponto Regency, South Sulawesi, Indonesia, covering the period 2021–2024. Key features included age in months, gender, weight, and height, while stunting status served as the target variable. Several machine learning algorithms were implemented, including Logistic Regression, Support Vector Machine, Multilayer Perceptron, K-Nearest Neighbors, Decision Tree, Random Forest, XGBoost, and Convolutional Neural Network. Data preprocessing involved imputation of missing values, feature encoding, and an 80/20 train-test split, while model interpretability was achieved using SHAP (SHapley Additive exPlanations) to provide both global and local feature attributions. The experimental results show that XGBoost achieved the highest accuracy of 97.57%, followed closely by Random Forest (97.28%) and Decision Tree (96.62%). SHAP analysis revealed that height was the most influential predictor, followed by age, gender, and weight, providing actionable insights for early identification of at-risk children. Local SHAP force plots further enabled case-level interpretation, enhancing the trustworthiness of the model in clinical or community health applications. The novelty of this research lies in integrating high-performing machine learning models with explainable AI for stunting prediction using minimal, easily collected health features in a resource-limited context. This framework not only improves the accuracy and transparency of early stunting detection but also provides a scalable approach to strengthen nutrition surveillance systems, with potential to inform targeted interventions and reduce the long-term impacts of childhood malnutrition.]]>
