ArticleHealthcare informatics research2023
Machine Learning-based Classifiers for the Prediction of Low Birth Weight.
Article in Healthcare informatics research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning for predictive risk stratification in recurrent miscarriage: a systematic review.BMC medical informatics and decision making · 2026Pooled it
- Machine-learning-based models for the optimization of post-cervical spinal laminoplasty outpatient follow-up schedules.BMC medical informatics and decision making · 2024Trial
- The Role of Maternal Homocysteine Concentration in Pregnancy Complications: A Systematic Review and Meta-Analysis.Journal of clinical medicine · 2026Review
- Article
- Unveiling socio-demographic determinants of low birth weight using machine learning techniques.PLOS global public health · 2026Article
- Prediction of birthweight with early and mid-pregnancy antenatal markers utilising machine learning and explainable artificial intelligence.Scientific reports · 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
- Deep learning for causal inference using low birth weight in midwife-led continuity care intervention in north Shoa zone, Ethiopia.Frontiers in artificial intelligence · 2025Article
- Predictive modeling of gestational weight gain: a machine learning multiclass classification study.BMC pregnancy and childbirth · 2024Article
- Ensemble machine learning framework for predicting maternal health risk during pregnancy.Scientific reports · 2024Article
- Prediction of COVID-19 patients' participation in financing informal care using machine learning methods: willingness to pay and willingness to accept approaches.BMC health services research · 2024Article
- Unraveling birth weight determinants: Integrating machine learning, spatial analysis, and district-level mapping.Heliyon · 2024Article
- Early prediction of low birth weight using boosting ensemble machine learning: A retrospective cohort study.Digital healthArticle
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Authors and funding
5 authors.
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Abstract
objectivesLow birth weight (LBW) is a global concern associated with fetal and neonatal mortality as well as adverse consequences such as intellectual disability, impaired cognitive development, and chronic diseases in adulthood. Numerous factors contribute to LBW and vary based on the region. The main objectives of this study were to compare four machine learning classifiers in the prediction of LBW and to determine the most important factors related to this phenomenon in Hamadan, Iran.
methodsWe carried out a retrospective cross-sectional study on a dataset collected from Fatemieh Hospital in 2017 that included 741 mother-newborn pairs and 13 potential factors. Decision tree, random forest, artificial neural network, support vector machine, and logistic regression (LR) methods were used to predict LBW, with five evaluation criteria utilized to compare performance.
resultsOur findings revealed a 7% prevalence of LBW. The average accuracy of all models was 87% or higher. The LR method provided a sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and accuracy of 74%, 89%, 7.04%, 29%, and 88%, respectively. Using LR, gestational age, number of abortions, gravida, consanguinity, maternal age at delivery, and neonatal sex were determined to be the six most important variables associated with LBW.
conclusionsOur findings underscore the importance of facilitating timely diagnosis of causes of abortion, providing genetic counseling to consanguineous couples, and strengthening care before and during pregnancy (particularly for young mothers) to reduce LBW.
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