One Decade of Artificial Intelligence (AI) Research in Public Health Stunting Prediction and Intervention

Abstract: Stunting attributable to malnutrition remains a global public health problem impacting the long-term physical and cognitive growth of children. In recent years, artificial intelligence (AI) has been applied in public health research to help diagnose and predict stunting. This study seeks to review trends in AI research on stunting prediction and intervention, and to identify existing challenges and opportunities. The articles were screened using the Systematic Literature Review (SLR) method with the PRISMA protocol through databases like PubMed, ScienceDirect, Scopus, and Google Scholar. The analysis of the data was performed using VOSviewer and Microsoft Excel. The results showed that the most used models in predicting stunting were Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting (XGBoost, LGBM), and Artificial Neural Network (ANN). Model evaluation is usually done through metrics such as AUC-ROC, accuracy, sensitivity, and specificity. Although AI has shown promise in identifying and predicting stunting, a few challenges remain: One is of data access and quality; others are model interpretability and integration within healthcare networks. Towards increasingly promising application outcomes: future directions for home-based health data prediction of the Internet of Things (IoT), Explainable AI (XAI), Multimodal AI, and natural language processing (NLP) models.

Nurjoko , Admi Syarif, Favoriten R. Lumbanraja and Khairun Nisa Berawi. “One Decade of Artificial Intelligence (AI) Research in Public Health Stunting Prediction and Intervention”. International Journal of Advanced Computer Science and Applications (IJACSA) 17.3 (2026). http://dx.doi.org/10.14569/IJACSA.2026.0170356

@article{2026,
title = {One Decade of Artificial Intelligence (AI) Research in Public Health Stunting Prediction and Intervention},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2026.0170356},
url = {http://dx.doi.org/10.14569/IJACSA.2026.0170356},
year = {2026},
publisher = {The Science and Information Organization},
volume = {17},
number = {3},
author = {Nurjoko and Admi Syarif and Favoriten R. Lumbanraja and Khairun Nisa Berawi}
}