Predicting Stunting in Rwanda Using Artificial Neural Networks: A Demographic Health Survey 2020 Analysis

ABSTRACT Background Stunting is a serious public health concern in Rwanda, affecting around 33.3% of children under the age of five in 2020. Several research employed machine learning algorithms to predict stunting in Rwanda; however, no study used artificial neural networks, despite their strong capacity to predict stunting. ANN is required to fill this need. 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 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 obtained, with an area under the receiver operating characteristic curve (AUC-ROC) of 0.84, indicating the model’s good performance. The ANN show good performance in stunting prediction. Several factors appear to contribute to the probability of stunting among the negative value aspects. First and foremost, the mother’s height is important, as a lower number suggests an increased risk of stunting in children. Positive value characteristics, on the other hand, emphasize elements that reduce the likelihood of stunting. Breastfeeding begins to emerge as a critical component, showing that early breastfeeding initiation has been linked with a decreased risk of stunting. Conclusion Our findings suggest that ANNs can be useful for predicting stunting in Rwanda and identifying the most important risk factors for stunting. These insights can inform targeted interventions to reduce the burden of stunting in Rwanda and other low- and middle-income countries.