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 traditional surveillance
methods proving inadequate for early detection and intervention. This study leverages advanced machine
learning and deep learning techniques to revolutionize stunting and wasting prediction in Malawi, utilizing
nationally representative World Bank’s Living Standards Measurement Surveys (LSMS) data to develop robust
predictive models capable of identifying at-risk children before clinical manifestations emerge. Seven classification algorithms were evaluated, including ensemble methods (Random Forest, XGBoost), Deep Neural Networks (DNN), and traditional approaches (SVM, Logistic Regression, KNN, Gradient Boosting). Class imbalance
challenges were addressed through SMOTE implementation and strategic class weighting. Model performance
was assessed using accuracy, precision, recall, F1-score, and AUC-ROC metrics across balanced datasets. Results
demonstrate exceptional predictive capabilities, with Random Forest achieving perfect performance for wasting
prediction (100 % accuracy, precision, recall, F1-score, and AUC-ROC) and near-perfect stunting classification
(99.98 % accuracy). XGBoost demonstrated comparable excellence with 99.49 % accuracy for wasting and 95.52
% for stunting prediction. DNN showed strong performance (91.50 % wasting accuracy, 76.64 % stunting accuracy), while traditional methods exhibited moderate effectiveness, with logistic regression achieving the
lowest performance (66.58 % wasting, 64.72 % stunting accuracy). These findings represent a paradigm shift
toward proactive nutritional surveillance, enabling early identification of vulnerable populations through datadriven approaches. The superior performance of ensemble algorithms provides policymakers with powerful tools
for evidence-based resource allocation and targeted interventions. Implementation of these predictive models
within Malawi’s health systems could significantly enhance early detection capabilities, facilitate timely nutritional interventions, and contribute substantially to achieving global nutrition targets while reducing childhood
mortality rates.