Evidence map›Paper›PMID 39981107›Full record

ArticleF1000Research2024

Predicting stunting status among under-5 children in Rwanda using neural network model: Evidence from 2020 Rwanda demographic and health survey.

Similien Ndagijimana, Ignace Kabano, Emmanuel Masabo, Jean Marie Ntaganda

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In one paragraph

Article in F1000Research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Similien NdagijimanaAfrican Centre of Excellence in Data Science, Kigali, Kigali, Rwanda.ORCID https://orcid.org/0000-0002-5636-3551
Ignace KabanoAfrican Centre of Excellence in Data Science, Kigali, Kigali, Rwanda.
Emmanuel MasaboAfrican Centre of Excellence in Data Science, Kigali, Kigali, Rwanda.
Jean Marie NtagandaCollege of Business and Economics, University of Rwanda, Kigali, Kigali, Rwanda.ORCID https://orcid.org/0000-0003-2464-2377

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Stunting is a serious public health concern in Rwanda, affecting around 33.3% of children under five in 2020. The researchers have employed machine learning algorithms to predict stunting in Rwanda; however, few studies used ANNs, despite their strong capacity to predict stunting. The purpose of this study was to predict stunting in Rwanda using ANNs and the most recent DHS data from 2020. Methods: DHS 2020 dataset was used to train and test an ANN model for predicting stunting in children. The dataset, which included various child, parental, and socio-demographic characteristics, was split into 80% training data and 20% testing and validation data. The model utilised a multilayer perceptron (MLP). Model performance was assessed using accuracy, precision, recall, and AUC-ROC. Feature importances were determined and highlighted the most critical predictors of stunting. Results: An overall accuracy of 72.0% on the test set was observed, with an AUC-ROC of 0.84, indicating the model's good performance. Factors appear to contribute to stunting among the negative value aspects. First and foremost, the mother's height is important, as a lower height suggests an increased risk of stunting in children. Positive value characteristics, on the other hand, emphasise elements that reduce the likelihood of stunting. The timing of the initiation of breastfeeding stands out as a crucial factor, showing that early breastfeeding initiation has been linked with a decreased risk of stunting. Conclusions: These findings suggest that ANNs can be a useful tool for predicting stunting in Rwanda and identifying the most important associated factors for stunting. These insights can inform targeted interventions to reduce the burden of stunting in Rwanda and other low- and middle-income countries. Potential targeted interventions include nutritional support programs for pregnant and lactating mothers, and providing educational programs for parents on nutrition and hygiene.

Indexed as

Growth DisordersHealth SurveysNeural Networks, ComputerChild, PreschoolFemaleHumansInfantMaleRwandaArtificial Neural NetworksChildrenFeature importanceRwanda.Stunting

Identifiers

PMID39981107
PMCPMC11840296

What OpenQuestion holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.