Advancing Predictive Analytics in Child Malnutrition: Machine, Ensemble and Deep Learning Models with Balanced Class Distribution for Early Detection of Stunting and Wasting

Child malnutrition remains a critical public health challenge in sub-Saharan Africa, with 2 traditional surveillance methods proving inadequate for early detection and intervention. This 3 study leverages advanced machine learning and deep learning techniques to revolutionize stunting 4 and wasting prediction in Malawi, utilizing nationally representative World Bank’s Living 5 Standards Measurement Surveys (LSMS) data to develop robust predictive models capable of 6 identifying at-risk children before clinical manifestations emerge. Seven classification algorithms 7 were evaluated, including ensemble methods (Random Forest, XGBoost), Deep Neural Networks 8 (DNN), and traditional approaches (SVM, Logistic Regression, KNN, Gradient Boosting). Class 9 imbalance challenges were addressed through SMOTE implementation and strategic class 10 weighting. Model performance was assessed using accuracy, precision, recall, F1-score, and 11 AUC-ROC metrics across balanced datasets. Results demonstrate exceptional predictive 12 capabilities, with Random Forest achieving perfect performance for wasting prediction (100% 13 accuracy, precision, recall, F1-score, and AUC-ROC) and near-perfect stunting classification 14 (99.98% accuracy). XGBoost demonstrated comparable excellence with 99.49% accuracy for 15 wasting and 95.52% for stunting prediction. DNN showed strong performance (91.50% wasting 16 accuracy, 76.64% stunting accuracy), while traditional methods exhibited moderate effectiveness, 17 with logistic regression achieving the lowest performance (66.58% wasting, 64.72% stunting 18 accuracy). These findings represent a paradigm shift toward proactive nutritional surveillance, 19 enabling early identification of vulnerable populations through data-driven approaches. The 20 superior performance of ensemble algorithms provides policymakers with powerful tools for 21 evidence-based resource allocation and targeted interventions. Implementation of these predictive 22 models within Malawi's health systems could significantly enhance early detection capabilities, 23 facilitate timely nutritional interventions, and contribute substantially to achieving global 24 nutrition targets while reducing childhood mortality rates. 100th Annual Conference, March 23-25, 2026, Wadham College, University of Oxford, Oxford, UK