This research work introduces a hybrid AI model comprising XGBoost for static prediction, LSTM networks for temporal forecasting, and SHAP for interpretable explainability. One of the major public health issues is retention in childhood malnutrition, especially in preschool children, which greatly impacts their growth, cognitive development and health conditions later in life. The conventional methods, based on descriptive statistical analysis and basic machine learning approaches, mainly utilise static predictors to classify malnutrition cases. Such practices tend to overlook the changes over time and do not offer any explanation, leading to their limited applications in preventive measure interactions. The model not only considers the current nutritional status but also predicts future trends by identifying important demographic and dietary components that underlie the predictions. The AI framework suggested produced 98.86% accuracy, AUC = 0.991, and RMSE = 0.082 when validated on CHNS data, which was significantly greater than the performance of Logistic Model Trees (91–95%) and Random Forests (94–98.6%), resulting in up to a 4.1% improvement. It thus confirmed the model's outstanding predictive capability. In contrast to previous works that were merely concerned with malnutrition type or anaemia classification, the present approach guarantees both accuracy and usability by integrating temporal prediction with Explainable AI. Consequently, the integration of predictive modelling with practical policy implications enables the early identification of children at risk and the implementation of targeted interventions, thereby providing a scalable and evidence-based contribution to childhood nutrition research. The combined model achieved an accuracy of 98.86%, an F1 score of 0.988, and an AUC of 0.991, which are extremely high values for predicting and highlighting the practicality of early detection and intervention in childhood malnutrition.
Xinming Zhang (Tue,) studied this question.