- Prediction of Stunting Among Under-5 Children in Rwanda Using Machine Learning Techniques
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Similien Ndagijimana, Ignace Habimana Kabano, Emmanuel Masabo, Jean Marie Ntaganda
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J Prev Med Public Health. 2023;56(1):41-49. Published online January 6, 2023
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DOI: https://doi.org/10.3961/jpmph.22.388
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 Abstract
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- 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.
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Summary
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Citations
Citations to this article as recorded by  - Predicting stunting in Rwanda using artificial neural networks: a demographic health survey 2020 analysis
Similien NDAGIJIMANA, Ignace KABANO, Emmanuel MASABO, Jean Marie NTAGANDA
F1000Research.2024; 13: 128. CrossRef - Development of a diagnostic predictive model for determining child stunting in Malawi: a comparative analysis of variable selection approaches
Jonathan Mkungudza, Halima S. Twabi, Samuel O. M. Manda
BMC Medical Research Methodology.2024;[Epub] CrossRef - Predicting harmful alcohol use prevalence in Sub-Saharan Africa between 2015 and 2019: Evidence from population-based HIV impact assessment
Mtumbi Goma, Wingston Felix Ng’ambi, Cosmas Zyambo, Yimam Getaneh Misganie
PLOS ONE.2024; 19(10): e0301735. CrossRef - Child stunting prevalence determination at sector level in Rwanda using small area estimation
Innocent Ngaruye, Joseph Nzabanita, François Niragire, Theogene Rizinde, Joseph Nkurunziza, Jean Bosco Ndikubwimana, Charles Ruranga, Ignace Kabano, Dieudonne N. Muhoza, Jeanine Ahishakiye
BMC Nutrition.2023;[Epub] CrossRef
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