Infant birth weight estimation and low birth weight classification in United Arab Emirates using machine learning algorithms

Khan, Wasif; Masud, Mohammad Mehedy; Zaki, Nazar; Ali, Luqman; Ali, Nasloon; Ahmed, Luai A.

doi:10.1038/s41598-022-14393-6

Cited by 32 publications

(26 citation statements)

References 42 publications

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“…Different classification approaches for LBW have been utilized in several studies. In the United Arab Emirates, Khan et al [ 20 ] assessed the performance of several ML algorithms. Through 5-fold cross-validation, they showed that the RF approach was superior to alternatives in birth weight estimation with regard to mean absolute error (294.53 g).…”

Section: Discussionmentioning

confidence: 99%

Machine Learning-based Classifiers for the Prediction of Low Birth Weight

et al. 2023

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Objectives: Low 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.Methods: We 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.Results: Our 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.Conclusions: Our 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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Section: Discussionmentioning

confidence: 99%

Machine Learning-based Classifiers for the Prediction of Low Birth Weight

et al. 2023

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“…Birth weight is stratified into low birth weight, normal birth weight and high birth weight if the baby weighs 2500grammes (g), 2500–3999 g and ≥4000 g, respectively 21 22…”

Section: Methodsmentioning

confidence: 99%

Prevalence and determinants of early neonatal mortality in Ethiopia: findings from the Ethiopian Demographic and Health Survey 2016

Tamir

Asmamaw

Negash

et al. 2023

bmjpo

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BackgroundEarly neonatal death accounts for a significant number of under-5 mortality worldwide. However, the problem is under-researched and under-reported in low-income and middle-income countries, particularly in Ethiopia. The magnitude of mortality during the early neonatal period and associated factors should be studied for designing appropriate policies, and strategies that could help tackle the problem. Hence, this study aimed to determine the prevalence and identify factors associated with early neonatal mortality in Ethiopia.MethodsThis study was conducted by using data from Ethiopian Demographic and Health Survey 2016. A total of 10 525 live births were enrolled in the study. A multilevel logistic regression model was used to identify determinants of early neonatal mortality. Adjusted OR (AOR) at a 95% CI was computed to assess the strength and significance of the association between outcome and explanatory variables. Factors with a p<0.05 were declared statistically significant.ResultsThe national prevalence of early neonatal mortality in Ethiopia was 41.8 (95% CI 38.1 to 45.8) early neonatal deaths per 1000 live births. The extreme ages of pregnancy (under 20 years (AOR 2.7, 95% CI 1.3 to 5.5) and above 35 years (AOR 2.4, 95% CI 1.5 to 4)), home delivery (AOR 2.4, 95% CI 1.3 to 4.3), low birth weight (AOR 3.3, 95% CI 1.4 to 8.2) and multiple pregnancies (AOR 5.3, 95% CI 4.1 to 9.9) were significantly associated early neonatal mortality.ConclusionsThis study revealed a higher prevalence of early neonatal mortality as compared with prevalence in other low-income and middle-income countries. Thus, it is determined to be essential to design maternal and child health policies and initiatives with a priority on the prevention of early neonatal deaths. Emphasis should be given to babies born to mothers at extreme ages of pregnancy, to those born of multiple pregnancies delivered at home and to low birthweight babies.

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“…Their high accuracy and low area under the receiver operating characteristic curve (AUROC) scores revealed that misleading performance remains a persistent issue [33]. The third group comprises studies that used data rebalancing techniques for their imbalanced data sets [30][31][32]. However, these studies only applied a single data rebalancing method to multiple ML models, either oversampling [30] or SMOTE [31,32].…”

Section: Related Workmentioning

confidence: 99%

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

Ren¹,

Wu²,

Tong³

et al. 2023

J Med Internet Res

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Background Low birthweight (LBW) is a leading cause of neonatal mortality in the United States and a major causative factor of adverse health effects in newborns. Identifying high-risk patients early in prenatal care is crucial to preventing adverse outcomes. Previous studies have proposed various machine learning (ML) models for LBW prediction task, but they were limited by small and imbalanced data sets. Some authors attempted to address this through different data rebalancing methods. However, most of their reported performances did not reflect the models’ actual performance in real-life scenarios. To date, few studies have successfully benchmarked the performance of ML models in maternal health; thus, it is critical to establish benchmarks to advance ML use to subsequently improve birth outcomes. Objective This study aimed to establish several key benchmarking ML models to predict LBW and systematically apply different rebalancing optimization methods to a large-scale and extremely imbalanced all-payer hospital record data set that connects mother and baby data at a state level in the United States. We also performed feature importance analysis to identify the most contributing features in the LBW classification task, which can aid in targeted intervention. Methods Our large data set consisted of 266,687 birth records across 6 years, and 8.63% (n=23,019) of records were labeled as LBW. To set up benchmarking ML models to predict LBW, we applied 7 classic ML models (ie, logistic regression, naive Bayes, random forest, extreme gradient boosting, adaptive boosting, multilayer perceptron, and sequential artificial neural network) while using 4 different data rebalancing methods: random undersampling, random oversampling, synthetic minority oversampling technique, and weight rebalancing. Owing to ethical considerations, in addition to ML evaluation metrics, we primarily used recall to evaluate model performance, indicating the number of correctly predicted LBW cases out of all actual LBW cases, as false negative health care outcomes could be fatal. We further analyzed feature importance to explore the degree to which each feature contributed to ML model prediction among our best-performing models. Results We found that extreme gradient boosting achieved the highest recall score—0.70—using the weight rebalancing method. Our results showed that various data rebalancing methods improved the prediction performance of the LBW group substantially. From the feature importance analysis, maternal race, age, payment source, sum of predelivery emergency department and inpatient hospitalizations, predelivery disease profile, and different social vulnerability index components were important risk factors associated with LBW. Conclusions Our findings establish useful ML benchmarks to improve birth outcomes in the maternal health domain. They are informative to identify the minority class (ie, LBW) based on an extremely imbalanced data set, which may guide the development of personalized LBW early prevention, clinical interventions, and statewide maternal and infant health policy changes.

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Infant birth weight estimation and low birth weight classification in United Arab Emirates using machine learning algorithms

Cited by 32 publications

References 42 publications

Machine Learning-based Classifiers for the Prediction of Low Birth Weight

Machine Learning-based Classifiers for the Prediction of Low Birth Weight

Prevalence and determinants of early neonatal mortality in Ethiopia: findings from the Ethiopian Demographic and Health Survey 2016

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

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