Optimizing Stunting Detection through SMOTE and Machine Learning: a Comparative Study of XGBoost, Random Forest, SVM, and k-NN - Sugihartono, Tri; Wijaya, Benny; Marini, Marini; Alkayess, Ahmad Paqih; Anugerah, Hendra Agustian (Bright Publisher, 2025)
Stunting is a vital public health priority that affects millions of children from all over the world, especially in developing countries, where chronic malnutrition impairs their physical growth and cognitive development. Early detection of stunting is necessary for its timely intervention to reduce long-lasting effects. The following study deals with the application of higher-end machine learning techniques in order to detect stunting with more accuracy, using XGBoost, Random Forest, SVM, and k-NN algorithms. Using a dataset sourced from Kaggle, containing 10,000 samples of anthropometric and demographic features, we addressed the significant class imbalance of the data; the number of samples representing stunted children was only 15% of the total. We surmounted this limitation using SMOTE to generate synthetic data in order to balance the representation for this minority class. Further feature selection to improve the performance and interpretability of the model was done using backward elimination, where less impactful features like