Improving the Accuracy of Stunting Prediction in Children in Pagar Alam City Using XGBoost Feature Selection and K-Nearest Neighbor Classification

Abstract

Stunting remains a major public health concern in Indonesia, including in Pagar Alam City. Early identification of at-risk children is essential to enable timely interventions and reduce long-term developmental consequences. However, predictive models such as K-Nearest Neighbor (K-NN) often experience reduced accuracy when faced with irrelevant features and imbalanced class distributions. This study integrates feature selection using Extreme Gradient Boosting (XGBoost) to enhance the predictive performance of K-NN in assessing stunting risk. Child growth data obtained from local health facilities were analyzed to build an initial baseline model, which exhibited limited accuracy due to excessive attributes and class imbalance. Through feature-importance analysis, XGBoost identified key predictors including sex, age, weight, and height. The optimized dataset was then used to retrain the K-NN model. Evaluation using accuracy, precision, recall, and F1-score demonstrated an improvement in accuracy from 85.63% to 93.72%. Beyond the computational results, this research provides significant contributions to the field of health informatics. The integration of XGBoost and K-NN offers an efficient analytical mechanism suitable for clinical decision support systems, particularly for data-driven screening in primary healthcare settings. The optimized, lightweight model can be embedded into health information systems to support child growth monitoring, strengthen evidence-based policymaking, and assist healthcare workers in targeting interventions more effectively. This approach can be replicated across other regions, supporting nationwide efforts to reduce stunting prevalence.