ArticleBMC medical informatics and decision making2022
Machine learning algorithms for predicting low birth weight in Ethiopia.
Article in BMC medical informatics and decision making, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed, 57 citations in OpenAlex.
- Predicting abnormal birth weight and identifying associated factors using machine learning in the Hararghe Health and Demographic Surveillance System, Ethiopia.BMC public health · 2026Article
- Prediction of early death in peripheral T-cell lymphoma-NOS patients based on machine learning.Annals of hematology · 2026Article
- Article
- Fairness in Low Birthweight Predictive Models: Implications of Excluding Race/Ethnicity.Journal of racial and ethnic health disparities · 2026Article
- Data science and artificial intelligence for maternal, newborn and child health: scoping review and thematic analysis.BMC public health · 2025Article
- Exploring the multifactorial predictors of stunting in children under five: A systematic review of the literature, 2015-2024.Journal of public health research · 2025Review
- 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
- Improving machine learning models through explainable AI for predicting the level of dietary diversity among Ethiopian preschool children.Italian journal of pediatrics · 2025Article
- Causal machine learning models for predicting low birth weight in midwife-led continuity care intervention in North Shoa Zone, Ethiopia.BMC medical informatics and decision making · 2025Article
- The Role of Digital Technology in Preventing Anemia Among Pregnant Women: A Scoping Review.International journal of telemedicine and applications · 2025Review
- Prediction of low birth weight from fetal ultrasound and clinical characteristics: a comparative study between a low- and middle-income and a high-income country.BMJ global health · 2024Article
- A computational model to analyze the impact of birth weight-nutritional status pair on disease development and disease recovery.Health information science and systems · 2024Article
- Prediction and feature selection of low birth weight using machine learning algorithms.Journal of health, population, and nutrition · 2024Article
- Predicting adverse birth outcome among childbearing women in Sub-Saharan Africa: employing innovative machine learning techniques.BMC public health · 2024Article
- Prediction of incomplete immunization among under-five children in East Africa from recent demographic and health surveys: a machine learning approach.Scientific reports · 2024Article
- Exploring machine learning algorithms to predict acute respiratory tract infection and identify its determinants among children under five in Sub-Saharan Africa.Frontiers in pediatrics · 2024Article
- Machine learning-based approach for predicting low birth weight.BMC pregnancy and childbirth · 2023Article
- Machine learning approach for the detection of vitamin D level: a comparative study.BMC medical informatics and decision making · 2023Article
- Issue of Data Imbalance on Low Birthweight Baby Outcomes Prediction and Associated Risk Factors Identification: Establishment of Benchmarking Key Machine Learning Models With Data Rebalancing Strategies.Journal of medical Internet research · 2023Article
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1 author at 1 institution in 1 country.
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Abstract
backgroundBirth weight is a significant determinant of the likelihood of survival of an infant. Babies born at low birth weight are 25 times more likely to die than at normal birth weight. Low birth weight (LBW) affects one out of every seven newborns, accounting for about 14.6 percent of the babies born worldwide. Moreover, the prevalence of LBW varies substantially by region, with 7.2 per cent in the developed regions and 13.7 per cent in Africa, respectively. Ethiopia has a large burden of LBW, around half of Africa. These newborns were more likely to die within the first month of birth or to have long-term implications. These are stunted growth, low IQ, overweight or obesity, developing heart disease, diabetes, and early death. Therefore, the ability to predict the LBW is the better preventive measure and indicator of infant health risks.
methodThis study implemented predictive LBW models based on the data obtained from the Ethiopia Demographic and Health Survey 2016. This study was employed to compare and identify the best-suited classifier for predictive classification among Logistic Regression, Decision Tree, Naive Bayes, K-Nearest Neighbor, Random Forest (RF), Support Vector Machine, Gradient Boosting, and Extreme Gradient Boosting.
resultsData preprocessing is conducted, including data cleaning. The Normal and LBW are the binary target category in this study. The study reveals that RF was the best classifier and predicts LBW with 91.60 percent accuracy, 91.60 percent Recall, 96.80 percent ROC-AUC, 91.60 percent F1 Score, 1.05 percent Hamming loss, and 81.86 percent Jaccard score.
conclusionThe RF predicted the occurrence of LBW more accurately and effectively than other classifiers in Ethiopia Demographic Health Survey. Gender of the child, marriage to birth interval, mother's occupation and mother's age were Ethiopia's top four critical predictors of low birth weight in Ethiopia.
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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.