Peer-reviewed article

A novel nutritional tool to identify infants at risk of stunting

Qian Wei, Dandan Su, Jihong Wei, Yaowu Zhan · 8 Jun 2026

Abstract

Objective: This study aimed to develop and validate a novel scoring system and prediction algorithm integrating growth, nutritional, and biochemical indicators for the identification of infant stunting, defined as length-for-age Z-score < -2 standard deviations according to the World Health Organization (WHO) Child Growth Standards. Methods: < 0.05). Multivariate logistic regression and Least Absolute Shrinkage and Selection Operator (LASSO) regression were subsequently used to select independent predictors and prevent overfitting. Three machine learning models-Random Forest, Gradient Boosting, and Support Vector Machine-were constructed. Model performance was evaluated using the Area Under the Receiver Operating Characteristic Curve (AUC), calibration curves, and Decision Curve Analysis. Interpretability was assessed via SHapley Additive exPlanations (SHAP) values. A visual nomogram was developed. Results: < 0.001). LASSO regression confirmed infant weight Z-score, length Z-score, length growth velocity, number of complementary food types, and Hb as the optimal variable combination. The Gradient Boosting model demonstrated superior performance, with an AUC of 0.861 (95% CI: 0.784-0.938) in the training set and 0.850 (95% CI: 0.699-1.000) in the internal validation set. Its calibration was excellent, and decision curve analysis indicated a higher net benefit across a wide risk threshold range. SHAP analysis identified infant weight Z-score as the most critical predictive variable. The nomogram provided a practical tool for quantitative risk assessment. Conclusion: The developed nutritional scoring system and Gradient Boosting prediction algorithm exhibited robust performance in identifying infants at risk of stunting. This tool facilitates early quantitative risk assessment and supports targeted clinical interventions.