Explainable Artificial Intelligence Using SHAP and Multilayer Perceptron for Transparent Stunting Risk Prediction in Sukoharjo, Indonesia

Abstract

Childhood stunting remains a critical public health challenge in Indonesia, with national prevalence at 19.8% in 2024 per SSGI data, hindering human capital development toward Indonesia Emas 2045. This study addresses the opacity of AI models in stunting prediction by integrating machine learning with Explainable AI (XAI) to enhance transparency for non-technical stakeholders. Using a survey dataset of 273 children from Sukoharjo Regency, risk factors encompassing key stunting determinants consist of maternal characteristics, household socioeconomic conditions, sanitation practices, and sociodemographic, were preprocessed via cleaning, label encoding, min-max scaling, and train-test split. Three classifiers; Logistic Regression (LR), Naïve Bayes (NB), and a Multilayer Perceptron (MLP)  with ReLU/softmax were trained and evaluated on accuracy, precision, recall, and F1–score. MLP with 16 hidden nodes, achieved the highest performance: 82% accuracy, 87% precision, 82% recall, and 82% F1-score, outperforming baselines. Kernel SHAP was applied to decompose predictions, revealing mother's education, age, number of children, birth length, household size, and income as top influencers. This XAI enhanced framework promotes trust and actionability in public health interventions, advancing informatics by bridging high accuracy neural networks models with interpretable insights for targeted stunting reduction in resource–limited settings.