Objectives: Rwanda reported a stunting rate of 33% in 2020, decreasing from 38% in 2015; however, stunting remains an issue. Globally, child deaths from malnutrition stand at 45%. The best options for the early detection and treatment of stunting should be made a community policy priority, and health services remain an issue. Hence, this research aimed to develop a model for predicting stunting in Rwandan children.Methods: The Rwanda Demographic and Health Survey 2019-2020 was used as secondary data. Stratified 10-fold cross-validation was used, and different machine learning classifiers were trained to predict stunting status. The prediction models were compared using different metrics, and the best model was chosen.Results: The best model was developed with the gradient boosting classifier algorithm, with a training accuracy of 80.49% based on the performance indicators of several models. Based on a confusion matrix, the test accuracy, sensitivity, specificity, and F1 were calculated, yielding the model’s ability to classify stunting cases correctly at 79.33%, identify stunted children accurately at 72.51%, and categorize non-stunted children correctly at 94.49%, with an area under the curve of 0.89. The model found that the mother’s height, television, the child’s age, province, mother’s education, birth weight, and childbirth size were the most important predictors of stunting status.Conclusions: Therefore, machine-learning techniques may be used in Rwanda to construct an accurate model that can detect the early stages of stunting and offer the best predictive attributes to help prevent and control stunting in under five Rwandan children.

Background Stunting is a serious public health concern in Rwanda, affecting around 33.3% of children under the age of five in 2020. Several examples of research have employed machine learning algorithms to predict stunting in Rwanda; however, no study used artificial neural networks (ANNs), despite their strong capacity to predict stunting. The purpose of this study was to predict stunting in Rwanda using ANNs and the most recent Demographic and Health Survey (DHS) data from 2020. Methods We used a multilayer perceptron (MLP) architecture to train and test the ANN model on a subset of the DHS dataset. The input variables for the model included child, parental and socio-demographic’s characteristics. The output variable was a binary indicator of stunting status (stunted vs. not stunted). Results An overall accuracy of 72.0% on the test set was observed, with an area under the receiver operating characteristic curve (AUC-ROC) of 0.84, indicating the model’s good performance. Several factors appear as important contributors to the probability of stunting among the negative value aspects. First and foremost, the mother’s height is important, as a lower height suggests an increased risk of stunting in children. Positive value characteristics, on the other hand, emphasie elements that reduce the likelihood of stunting. The timing of the initiation of breastfeeding stands out as a crucial factor, showing that early breastfeeding initiation has been linked with a decreased risk of stunting. Conclusions Our findings suggest that ANNs can be a useful tool for predicting stunting in Rwanda and identifying the most important associated factors for stunting. These insights can inform targeted interventions to reduce the burden of stunting in Rwanda and other low- and middle-income countries.

Background Stunting is a serious public health concern in Rwanda, affecting around 33.3% of children under five in 2020. The researchers have employed machine learning algorithms to predict stunting in Rwanda; however, few studies used ANNs, despite their strong capacity to predict stunting. The purpose of this study was to predict stunting in Rwanda using ANNs and the most recent DHS data from 2020. Methods DHS 2020 dataset was used to train and test an ANN model for predicting stunting in children. The dataset, which included various child, parental, and socio-demographic characteristics, was split into 80% training data and 20% testing and validation data. The model utilised a multilayer perceptron (MLP). Model performance was assessed using accuracy, precision, recall, and AUC-ROC. Feature importances were determined and highlighted the most critical predictors of stunting. Results An overall accuracy of 72.0% on the test set was observed, with an AUC-ROC of 0.84, indicating the model’s good performance. Factors appear to contribute to stunting among the negative value aspects. First and foremost, the mother’s height is important, as a lower height suggests an increased risk of stunting in children. Positive value characteristics, on the other hand, emphasise elements that reduce the likelihood of stunting. The timing of the initiation of breastfeeding stands out as a crucial factor, showing that early breastfeeding initiation has been linked with a decreased risk of stunting. Conclusions These findings suggest that ANNs can be a useful tool for predicting stunting in Rwanda and identifying the most important associated factors for stunting. These insights can inform targeted interventions to reduce the burden of stunting in Rwanda and other low- and middle-income countries. Potential targeted interventions include nutritional support programs for pregnant and lactating mothers, and providing educational programs for parents on nutrition and hygiene.

Background: Malnutrition is a major public health concern worldwide; a recent study found that 22% of children under the age of five were stunted worldwide in 2020. Stunting in Rwanda has decreased dramatically over the last 15 years, from 51% in 2005 to 33% by 2020. However, because few geospatial studies have been conducted, geographical survey data analysis is required to effectively focus stakeholders' efforts in response to successful stewardship of health programs in the eradication of all types of malnutrition. The study's goal is to map the prevalence distribution of stunted children under the age of five, make Projections, and provide exceedance probability maps for each stunting at the 30% threshold value, as well as identify risk factors associated with stunting in Rwanda. Methods: This study makes use of Rwandan Demographic and Health Surveys (R-DHS)2019/2020. To obtain the marginal posterior distribution of stunting prevalence at each location in Rwanda, the Bayesian model was developed using an integrated nested Laplace approximation technique. The risk factors for stunting were identified using multivariate logistic regression. Results: This research finds the prevalence of stunting in 500 clusters. Kigali city clusters had the lowest prevalence, ranging from 0% to 25%. The Western Province, specifically the Congo Nile Divide, has the highest rate where some clusters have more than 60%. The 30% probability threshold enables the identification of Rwandan areas and communities most vulnerable to stunting. In Rwanda, regional disparities in childhood stunting are significant. There is statistically significant child, maternal, and socio-demographic characteristics with p-values less than 5% in a 95% confidence interval (CI). Among the risk factors are the baby's birth weight (OR:2.18, CI: [1.613-2.95], intestinal parasites (OR:1.544, 95% CI: [1.253-1.903], and the baby's age (OR:1.095, 95% CI: [1.024-1.171]. Province and altitude have ORs of 1.088 and 1.575, respectively, in socio-demographic factors. Conclusions: Finally, geospatial studies should be conducted to identify locations that require more attention in disease and epidemic control and multivariate logistic regression to identify risk factors. Therefore, more attention of malnutrition eradication should be paid to Rwanda's Western and Northern Provinces. The findings of this study may be useful to program managers and decision makers working to reduce the burden of stunting.

Purpose: In Rwanda, childhood stunting is a major public health problem. Earlier studies employed traditional statistical approaches to identify causal factors to stunting, and little is known about the uses and effectiveness of machine learning (ML) algorithms that may identify risk factors for a variety of health conditions based on complex data. Methods: This study examines the usefulness of machine learning algorithms in predicting stunting in children under the age of five using data from the 2020 Rwanda Demographic and Health Survey. Random Forest was utilized for feature selection, and supervised machine learning methods were applied. The confusion matrix and Receiver Operating Characteristics (ROC), which incorporated several metrics, were used to evaluate the performance of algorithms. Additionally, the outperformed model identifies variables that strongly predict stunting in Rwanda. Ultimately, multivariate logistic regression was used. Results: The XGBoost classifier predicts stunting with the lowest misclassification error among the selected ML algorithms, followed by a gradient boosting classifier, random forests, support vector machines, classification trees, and logistic regression with forward-stepwise selection. The 10 most important variables in predicting childhood stunting in Rwanda are breastfeeding start, mother’s height, provinces, possessing television, child size at birth, maternal education, maternal BMI, wealth index, preceding birth interval, and child age. Conclusion: This study contributes to the body of knowledge that confirmed the efficacy of ML for population health research and policy decision-making in a broad range of areas, including defining treatment effects in epidemiological studies and child undernutrition. This study shows that the XGBoost classifer is highly recommended because of the combination of flexibility, scalability,regularization, ensemble approaches, and feature importance that distinguishesXGBoost from gradient-based classifers.

Background: Diarrhoeal disease remains a leading cause of morbidity and mortality among children under five years (U5) in Rwanda, contributing substantially to healthcare utilisation and imposing considerable economic burdens on households and the health system. National estimates indicate that approximately 14.3% of children under five experienced diarrhoea, and diarrhoea has been reported as the third leading cause of death in this age group. Climate variability may influence diarrhoeal disease risk, but evidence across Rwanda’s diverse ecological zones remains limited. This study examined sector-level associations between climatic variables and U5 diarrhoeal incidence in Rwanda. Methods: Monthly counts of U5 diarrhoeal cases reported by health facilities across 416 administrative sectors in Rwanda from January 2015 to December 2024 were analysed together with satellite-derived climate data. Spatio-temporal statistical models were used to evaluate associations between standardised climate variables and diarrhoeal incidence rates while accounting for geographic and seasonal variation. Model comparison relied on both goodness-of-fit metrics and out-of-sample predictive diagnostics. Results: Diarrhoeal incidence showed clear spatial and seasonal patterns, with persistently higher rates in northern and eastern Rwanda. In the final model, maximum temperatures were positively associated with increased diarrhoeal incidence, with a one-standard-deviation increase corresponding to a 5.6% rise in the estimated incidence rate ratio (RR = 1.056; 95% CrI: 1.02–1.09). Relative humidity showed a protective association (RR ≈ 0.919; 95% CrI: 0.89–0.94), while rainfall showed limited immediate effects. Conclusion: Under-five diarrhoeal incidence in Rwanda showed marked spatial and seasonal variability. Maximum temperature was positively associated with diarrhoeal incidence rates, while relative humidity showed an inverse association that may partly reflect unmeasured confounding by drought, water access, and WASH infrastructure. External validation is needed before the modelling framework can be applied operationally. These findings highlight the importance of preparedness during hotter periods and strengthened prevention and surveillance efforts in high-risk areas.