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Original Articles
- Prediction of Stunting Among Under-5 Children in Rwanda Using Machine Learning Techniques
- Similien Ndagijimana, Ignace Habimana Kabano, Emmanuel Masabo, Jean Marie Ntaganda
- J Prev Med Public Health. 2023;56(1):41-49. Published online January 6, 2023
- DOI: https://doi.org/10.3961/jpmph.22.388
- 3,942 View
- 299 Download
- 1 Web of Science
- 2 Crossref
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Abstract
PDF - 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. -
Summary
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 - 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
- Prediction Equations for FVC and FEV1 among Korean Children Aged 12 Years.
- Jong Won Kang, Yeong Su Ju, Joohon Sung, Soo Hun Cho
- Korean J Prev Med. 1999;32(1):60-64.
- 2,182 View
- 31 Download
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Abstract
PDF
- OBJECTIVES
Changes in lung function are frequently used as biological markers to assess the health effects of criteria air pollutants. We tried to formulate the prediction models of pulmonary functions based on height, weight, age and gender, especially for children aged 12 years who are commonly selected for the study of health effects of the air pollution. METHODS: The target pulmonary function parameters were forced vital capacity(FVC) and forced expiratory volume in one second(FEV1). Two hundreds and fifity-eight male and 301 female 12-year old children were included in the analysis after excluding unsatisfactory tests to the criteria recommended by American Thoracic Sosiety and excluding more or less than 20% predicted value by previous prediction equations. The weight prediction equation using height as a independent variable was calculated, and then the difference of observed weight and predicted weight (i.e. residual) was used as the independent variable of pulmonary function prediction equations with height. RESULTS: The prediction equations of FVC and FEV1 for male are FVC(ml) = 50.84 x height(cm) + 7.06 x weight residual - 4838.86, FEV1(ml) = 43.57 x height(cm) + 3.16 x weight residual - 4156.66, respectively. The prediction equations of FVC and FEV1 for female are FVC(ml) = 42.57 x height(cm) + 12.50 x weight residual - 3862.39, FEV1(ml) = 36.29 x height(cm) + 7.74 x weight residual - 3200.94, respectively. -
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