Predictive Modeling to Identify Stunting Risk in Children

Abstract

Background: Indonesia still experiences a high stunting burden. This has both short- and long-term impacts, including higher morbidity and mortality, impaired future growth, increased chronic disease risk, and reduced productivity later in life. Objective: This paper aims to assess the main risk factors associated with stunting in Indonesia and to develop a predictive model to identify stunting risk in children. Methods: Data from the 2018 Indonesian Basic Health Research database were analyzed for children aged under 5 years (n = 13 106) and their mothers. Bivariate analysis was used to select variables significantly associated with stunting risk. A decision tree model was then applied to predict the risk of stunting by age group, and the data were plotted into a receiver operating characteristic (ROC) curve. Results: The stunting rate reached 25.8%. Based on the decision tree, age, sex, birth weight, birth length, mother's highest level of education, handwashing habits, and exclusive breastfeeding were found to impact stunting risk. The prediction model demonstrated an accuracy of 73.8% for assessing the risk of stunting. The ROC curve showed an area under the curve of 63.7%, with a sensitivity of 60.1% and specificity of 59.8%. Conclusions: This prediction model is accurate for assessing the risk of stunting. The decision tree-based prediction model performs reasonably well in differentiating between stunted and non-stunted children across different age groups, as indicated by the ROC curve.