ArticlePloS one2022
Predicting risks of low birth weight in Bangladesh with machine learning.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 2 of them syntheses that pooled it.
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Who cites it
37 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Methodological conduct and risk of bias in studies on prenatal birthweight prediction models using machine learning techniques: a systematic review.BMC pregnancy and childbirth · 2025Pooled it
- Machine Learning in Predicting Child Malnutrition: A Meta-Analysis of Demographic and Health Surveys Data.International journal of environmental research and public health · 2025Pooled it
- Application of Machine Learning Algorithms for Predicting Infant Mortality in India: An Analysis of the National Family Health Survey-5, 2019-2021.Healthcare informatics research · 2026Article
- Predicting abnormal birth weight and identifying associated factors using machine learning in the Hararghe Health and Demographic Surveillance System, Ethiopia.BMC public health · 2026Article
- Predicting low birth weight in Bangladesh using interpretable machine learning models.BMC pregnancy and childbirth · 2026Article
- Prevalence and risk factors of low birth weight in a rural district of Bangladesh: a prospective cohort study.BMC pediatrics · 2026Article
- Article
- Prevalence and determinants of low birth weight and its association with child malnutrition in Bangladesh.BMC nutrition · 2026Article
- Using DHIS2 routine data for health system preparedness in resource-limited settings: A Bayesian predictive approach in Bangladesh.PLOS global public health · 2026Article
- Predictive modeling with machine learning for low birth weight in the state of São Paulo: a retrospective population-based study.Sao Paulo medical journal = Revista paulista de medicina · 2026Article
- Exploring the multilevel determinants of low birth weight in Bangladesh: Understanding implications for targeted public health interventions.PLOS global public health · 2026Article
- Unveiling socio-demographic determinants of low birth weight using machine learning techniques.PLOS global public health · 2026Article
- Predictive Modelling of Low Birth Weight in Pregnancies: A Comparative Analysis of Logistic Regression and Decision Tree Approaches.Indian journal of community medicine : official publication of Indian Association of Preventive & Social Medicine · 2025Article
- Estimating the impact of maternal anemia on low-birth-weight in Sub-Saharan African countries: propensity score matching analysis.BMC pregnancy and childbirth · 2025Article
- Prediction of birthweight with early and mid-pregnancy antenatal markers utilising machine learning and explainable artificial intelligence.Scientific reports · 2025Article
- Low birth weight risk prediction model: a prognostic study in the Birhan field site in Ethiopia.Journal of global health · 2025Article
- Predicting Low Birth Weight in Big Cities in the United States Using a Machine Learning Approach.International journal of environmental research and public health · 2025Article
- Predicting low birth weight risks in pregnant women in Brazil using machine learning algorithms: data from the Araraquara cohort study.BMC pregnancy and childbirth · 2025Article
- Utilization of tree-based machine learning models for predicting low birth weight cases.BMC pregnancy and childbirth · 2025Article
- Machine learning based prediction of low birth weight and its associated risk factors: Insights from the Bangladesh Demographic and Health Survey 2022.PLOS global public health · 2025Article
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5 authors.
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Abstract
BACKGROUND AND
objectiveLow birth weight is one of the primary causes of child mortality and several diseases of future life in developing countries, especially in Southern Asia. The main objective of this study is to determine the risk factors of low birth weight and predict low birth weight babies based on machine learning algorithms. MATERIALS AND
methodsLow birth weight data has been taken from the Bangladesh Demographic and Health Survey, 2017-18, which had 2351 respondents. The risk factors associated with low birth weight were investigated using binary logistic regression. Two machine learning-based classifiers (logistic regression and decision tree) were adopted to characterize and predict low birth weight. The model performances were evaluated by accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve.
resultsThe average percentage of low birth weight in Bangladesh was 16.2%. The respondent's region, education, wealth index, height, twin child, and alive child were statistically significant risk factors for low birth weight babies. The logistic regression-based classifier performed 87.6% accuracy and 0.59 area under the curve for holdout (90:10) cross-validation, whereas the decision tree performed 85.4% accuracy and 0.55 area under the curve.
conclusionsLogistic regression-based classifier provided the most accurate classification of low birth weight babies and has the highest accuracy. This study's findings indicate the necessity for an efficient, cost-effective, and integrated complementary approach to reduce and correctly predict low birth weight babies in Bangladesh.
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