Peer-reviewed article
Predictive Modeling to Identify Stunting Risk in Children.
Siti Rahayu Nadhiroh, Armedy Ronny Hasugian, Allisa Nadhira Permata Arinda Putri, Su Peng Loh, Stefania Widya Setyaningtyas et al. ·
Summary
Written by AI, not yet AICS's view · claude-haiku-4-5-20251001This study analyzed data from over 13,000 Indonesian children under 5 years old to identify risk factors for stunting and develop a predictive model. The research found that stunting affected 25.8% of children in the sample, with key risk factors including age, sex, birth weight, birth length, maternal education, handwashing habits, and exclusive breastfeeding. A decision tree model was created to predict stunting risk with 73.8% accuracy. The model could help identify high-risk children for early intervention.
Key points
- Stunting prevalence in Indonesia was 25.8% among children under 5
- Seven factors significantly associated with stunting: age, sex, birth weight, birth length, mother's education, handwashing habits, and exclusive breastfeeding
- The predictive model achieved 73.8% accuracy in assessing stunting risk
- ROC curve showed area under the curve of 63.7%, sensitivity of 60.1%, and specificity of 59.8%
- The model performed reasonably well in differentiating stunted from non-stunted children across different age groups
Limitations
The study uses a predictive model rather than testing an intervention, so it cannot tell us whether any actions to address the identified risk factors would actually reduce stunting. The model's moderate accuracy (63.7% AUC) suggests it may miss some cases or incorrectly identify risk in practice.
Abstract
BackgroundIndonesia 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.ObjectiveThis 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.MethodsData 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.ResultsThe 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%.ConclusionsThis 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.