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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
  • 3,348 View
  • 273 Download
  • 1 Web of Science
  • 2 Crossref
AbstractAbstract 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
The Effect of the Physical Factors of Parents and Children on Stunting at Birth Among Newborns in Indonesia
Kencana Sari, Ratu Ayu Dewi Sartika
J Prev Med Public Health. 2021;54(5):309-316.   Published online August 29, 2021
DOI: https://doi.org/10.3961/jpmph.21.120
  • 4,992 View
  • 423 Download
  • 5 Crossref
AbstractAbstract PDF
Objectives
This study examined stunting at birth and its associations with physical factors of parents and children in Indonesia.
Methods
This study analyzed secondary data from the national cross-sectional Indonesian Basic Health Survey 2018, conducted across 34 provinces and 514 districts/cities. Birth length data were available for 756 newborns. Univariable, bivariable, and multivariable logistic regression analyses were performed to determine associations between the physical factors of parents and children and stunting at birth.
Results
In total, 10.2% of children aged 0 months were stunted at birth (10.7% of males and 9.5% of females). Stunting at birth was associated with the mother’s age at first pregnancy, parity, parents’ heights, parents’ ages, and gestational age. Children from mothers with short statures (height <145.0 cm) and fathers with short statures (height <161.9 cm) had an almost 6 times higher likelihood of being stunted at birth (adjusted odds ratio, 5.93; 95% confidence interval, 5.53 to 6.36). A higher maternal age at first pregnancy had a protective effect against stunting. However, other variables (firstborn child, preterm birth, and both parents’ ages being <20 or >35 years) corresponded to a 2-fold higher likelihood of stunting at birth compared to the reference.
Conclusions
These findings provide evidence that interventions to reduce stunting aimed at pregnant females should also consider the parents’ stature, age, and parity, particularly if it is the first pregnancy and if the parents are short in stature or young. Robust programs to support pregnant females and monitor children’s heights from birth will help prevent intergenerational stunting.
Summary

Citations

Citations to this article as recorded by  
  • How do household living conditions and gender-related decision-making influence child stunting in Rwanda? A population-based study
    Jean Nepo Utumatwishima, Ingrid Mogren, Aline Umubyeyi, Ali Mansourian, Gunilla Krantz, Olutosin Ademola Otekunrin
    PLOS ONE.2024; 19(3): e0290919.     CrossRef
  • Stunting at birth: linear growth failure at an early age among newborns in Hawassa city public health hospitals, Sidama region, Ethiopia: a facility-based cross-sectional study
    Haileyesus Ejigu, Zelalem Tafese
    Journal of Nutritional Science.2023;[Epub]     CrossRef
  • Socio-economic and agricultural factors associated with stunting of under 5-year children: findings from surveys in mountains, dry zone and delta regions of rural Myanmar (2016–2017)
    Min Kyaw Htet, Tran Thanh Do, Thet Wah, Thant Zin, Myat Pan Hmone, Shahreen Raihana, Elizabeth Kirkwood, Lwin Mar Hlaing, Michael J Dibley
    Public Health Nutrition.2023; 26(8): 1644.     CrossRef
  • Predictor of Stunting Among Children 0-24 Months Old in Indonesia: A Scoping Review
    Via Eliadora Togatorop, Laili Rahayuwati, Raini Diah Susanti
    Jurnal Obsesi : Jurnal Pendidikan Anak Usia Dini.2023; 7(5): 5654.     CrossRef
  • Determinants of Incident Stunting in Elementary School Children in Endemic Area Iodine Deficiency Disorders Enrekang Regency
    Nur Abri, Saifuddin Sirajuddin, Burhanuddin Bahar, Nurhaedar Jafar, Syamsiar S. Russeng, Zakaria Zakaria, Veni Hadju, Abdul Salam, Abdul Razak Thaha
    Open Access Macedonian Journal of Medical Sciences.2022; 10(E): 161.     CrossRef

JPMPH : Journal of Preventive Medicine and Public Health