ArticleScientific reports2022
Infant birth weight estimation and low birth weight classification in United Arab Emirates using machine learning algorithms.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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Who cites it
28 citing papers in PubMed, 1 synthesis or guideline 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
- 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
- Article
- Machine learning versus traditional formulas for fetal weight estimation: An international multicenter study evaluating prediction accuracy across birth weight percentiles.International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics · 2026Article
- Fairness in Low Birthweight Predictive Models: Implications of Excluding Race/Ethnicity.Journal of racial and ethnic health disparities · 2026Article
- Early prediction of very and extreme preterm births using a one-class classification framework on electronic health records in UAE.Scientific reports · 2025Article
- Interpretable machine learning model for predicting low birth weight in singleton pregnancies: a retrospective cohort study.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
- Predicting abnormal fetal growth using deep learning.NPJ digital medicine · 2025Article
- Risk-based evaluation of machine learning-based classification methods used for medical devices.BMC medical informatics and decision making · 2025Article
- Utilization of tree-based machine learning models for predicting low birth weight cases.BMC pregnancy and childbirth · 2025Article
- An efficient interpretable framework for unsupervised low, very low and extreme birth weight detection.PloS one · 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
- Article
- 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
- A Systematic Review of the Outcomes of Utilization of Artificial Intelligence Within the Healthcare Systems of the Middle East: A Thematic Analysis of Findings.Health science reports · 2024Review
- Identifying First-Trimester Risk Factors for SGA-LGA Using Weighted Inheritance Voting Ensemble Learning.Bioengineering (Basel, Switzerland) · 2024Article
- Multimodal Early Birth Weight Prediction Using Multiple Kernel Learning.Sensors (Basel, Switzerland) · 2023Article
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7 authors.
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Abstract
Accurate prediction of a newborn's birth weight (BW) is a crucial determinant to evaluate the newborn's health and safety. Infants with low BW (LBW) are at a higher risk of serious short- and long-term health outcomes. Over the past decade, machine learning (ML) techniques have shown a successful breakthrough in the field of medical diagnostics. Various automated systems have been proposed that use maternal features for LBW prediction. However, each proposed system uses different maternal features for LBW classification and estimation. Therefore, this paper provides a detailed setup for BW estimation and LBW classification. Multiple subsets of features were combined to perform predictions with and without feature selection techniques. Furthermore, the synthetic minority oversampling technique was employed to oversample the minority class. The performance of 30 ML algorithms was evaluated for both infant BW estimation and LBW classification. Experiments were performed on a self-created dataset with 88 features. The dataset was obtained from 821 women from three hospitals in the United Arab Emirates. Different performance metrics, such as mean absolute error and mean absolute percent error, were used for BW estimation. Accuracy, precision, recall, F-scores, and confusion matrices were used for LBW classification. Extensive experiments performed using five-folds cross validation show that the best weight estimation was obtained using Random Forest algorithm with mean absolute error of 294.53 g while the best classification performance was obtained using Logistic Regression with SMOTE oversampling techniques that achieved accuracy, precision, recall and F1 score of 90.24%, 87.6%, 90.2% and 0.89, respectively. The results also suggest that features such as diabetes, hypertension, and gestational age, play a vital role in LBW classification.
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