Prediction of Stunting Among Under-5 Children in Rwanda Using Machine Learning Techniques

Abstract

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.

Predicting stunting in Rwanda using artificial neural networks: a demographic health survey 2020 analysis

“…32 Being at higher altitudes is associated with a high risk of stunting in children in Rwanda as revealed by the study. 11 It is crucial to emphasise that altitude is only one of several factors that contribute to stunting, and its influence varies depending on other contextual factors such as economic status, healthcare facilities, and dietary choices. 33 To alleviate stunting in high-altitude locations, a comprehensive approach is required, which includes increasing access to healthcare, nutrition, sanitation, and education, as well as addressing the underlying socioeconomic determinants of health, 34 this study also confirm the findings shown in the different studies.…”

“…10 A few studies have been conducted in Rwanda using other machine learning technics like logistic regression, Supportive Vector Machine (SVM), Naive Bayes Random Forest (RF), XGBoost gradient model. 11 However, based on the existing knowledge there has been few researches in Rwanda that attempted to utilise ML to predict stunting like the study conducted by Similien et al 11 ANNs have proven to be highly effective in predicting illnesses. However, Similien's publication did not delve into the utilisation of ANNs for this purpose, despite their demonstrated effectiveness in prediction.…”

“…Given the crucial importance of addressing the root causes of stunting for effective treatments and policymaking, the researcher chose to conduct this study on the application of ANNs in the specific context of stunting in Rwanda, using the same dataset as the aforementioned publication. 11 The remaining party of this study is organised as follow: Methods, Results, Discussion, Conclusion and recommendation.…”

Machine Intelligence in Africa: a survey

“…The paper in [276] employs a machine learning approach to predict the demand for essential medicines in Rwanda based on consumption data. The research outlined in [277] utilizes machine learning techniques for predicting stunting among under-5 children in Rwanda. [278] explores the early detection of students at risk of poor performance in Rwanda's higher education using machine learning techniques.…”

Machine Learning Model For Stunting Prediction

“…The contribution of this study is to propose an ML model that performs better for stunting detection (Ndagijimana et al 2023). This ML model includes algorithm selection, oversampling, feature selection, feature weighting, data distribution, and determining algorithm parameters (Sultana and Islam 2023).…”