Gestational Age and Pregnancy Outcomes

Neggers, Yasmin H.

doi:10.5772/intechopen.72419

Cited by 5 publications

(5 citation statements)

References 35 publications

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“…This study revealed that Arab mothers were twice as likely to deliver a preterm baby compared to non-Arab mothers. In line with the current results, several studies showed similar findings [27,28]. Some of the important pregnancy outcomes, such as preterm birth and LBW, were found to vary by nationality [28].…”

Section: Discussionsupporting

confidence: 91%

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Factors Associated with Preterm Birth and Low Birth Weight in Abu Dhabi, the United Arab Emirates

Taha

Hassan²,

Wikkeling-Scott

et al. 2020

IJERPH

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Both preterm birth and low birth weight (LBW) represent major public health problems worldwide due to their association with the catastrophic effects of morbidity and mortality. Few data exist about such adverse pregnancy outcomes. The current study aimed to investigate the prevalence of and factors associated with preterm birth and LBW among mothers of children under two years in Abu Dhabi, United Arab Emirates. Data were collected in clinical and non-clinical settings across various geographical areas in Abu Dhabi. The data were analyzed using both descriptive and inferential statistics. A total of 1610 mother–child pairs were included in the current study. Preterm birth rate was 102 (6.3%) with a 95% confidence interval [CI] (6.1%, 6.5%) and the LBW rate was 151 (9.4%) with a 95% CI (9.3%, 9.5%). The mean (SD) of gestational age (GA) and birth weight at delivery was 39.1 (1.9) weeks and 3080.3 (518.6) grams, respectively. Factors that were positively associated with preterm birth were Arab mothers, maternal education level below secondary, caesarean section, and LBW. LBW was associated with female children, caesarean section (CS), first child order, and preterm birth. The current study highlighted the need for further interventional research to tackle these public health issues such as reducing the high CS rate and improving maternal education.

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Section: Discussionsupporting

confidence: 91%

“…In line with the current results, several studies showed similar findings [27,28]. Some of the important pregnancy outcomes, such as preterm birth and LBW, were found to vary by nationality [28]. Such differences among nationalities require more investigation, especially in a country like the UAE with multiple nationalities [18].…”

Section: Discussionsupporting

confidence: 85%

Factors Associated with Preterm Birth and Low Birth Weight in Abu Dhabi, the United Arab Emirates

Taha

Hassan²,

Wikkeling-Scott

et al. 2020

IJERPH

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“…It is measured in weeks from the first day of the woman's last menstrual cycle to the current date. A normal pregnancy can range from 38 to 42 weeks 78 9th feature : “Birth_number” represents the number of births of the mother. 10th feature : The “Autism” feature is presented by two categories (0 for “no autism” and 1 for “autism”), indicating infectious status. …”

Section: Methodsmentioning

confidence: 99%

“…A normal pregnancy can range from 38 to 42 weeks. 78 7. 9th feature: "Birth_number" represents the number of births of the mother.…”

Section: Dataset Featuresmentioning

confidence: 99%

Towards physician's experience: Development of machine learning model for the diagnosis of autism spectrum disorders based on complex T‐spherical fuzzy‐weighted zero‐inconsistency method

Albahri

Zaidan

Alsattar³

et al. 2022

Computational Intelligence

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Autism spectrum disorders (ASD) are a diverse group of conditions characterized by difficulty with social interaction and communication. ASD is expected to be a high‐risk disease. Recent studies have focused on the diagnosis based on sociodemographic and family characteristics factors. The development of a diagnosis model, which is primarily based on machine learning methods, has been carried out to alleviate the detection of autism. However, they neglected the importance of ASD features in a training dataset, especially because some features have different levels of contributions to the processing data and possess more relevancies to the classification information than others. Such limitations use preprocessing techniques for the construction of the machine learning model, but the role of the physician's experience towards feature contributions remains limited. However, for certain autism datasets, the relevancies of sociodemographic and family characteristic feature concerning the given class labels should be considered. Accordingly, this study developed a new machine learning model for the diagnosis of ASD based on multi‐criteria decision‐making (MCDM). By using three methodology phases, the model combines two representative theories, namely, MCDM and machine learning. The identification phase for imbalance ASD dataset and application of pre‐possessing stages by imputing missing values, feature selection of sociodemographic and family characteristics, and data imbalanced approach resulted in balanced ASD dataset, including 107,573 cases. The development phase for the new model was achieved by the proposed complex T‐spherical fuzzy‐weighted zero‐inconsistency (CT‐SFWZIC) method. CT‐SFWZIC was developed based on a new fuzzy set (i.e., complex T‐spherical fuzzy) for weighting affected features, and then applied for training and testing the machine learning model considering various complex T‐spherical fuzzy membership functions (i.e., T = 1, 2, 3, 5, 7, and 10). The results obtained from a 10‐fold cross‐validation test for all T values by using nine machine learning classifiers were measured under seven evaluation metrics, namely AUC, accuracy, F1, precision, recall, training time (s), and test time (s). Performance evaluation results reveal that AdaBoost can be used to boost the ASD diagnosis as the best machine learning algorithm for all T values based on all metrics to improve the diagnosis based on physician's assessment. Under the most extreme evaluation metric, which is accuracy, the results of the AdaBoost classifiers for T = 1, 2, 3, 5, 7, 10 have obtained 0.99948, 0.99934, 0.99930, 0.99939, 0.99910, and 0.99930, respectively.

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“…The diagnostic criteria for PE were updated by the American College of Obstetricians and Gynecologists (ACOG) in 2020 and The International Society for the Study of Hypertension in Pregnancy (ISSHP) in 2014 to include hypertension in the absence of proteinuria if there is also evidence of systemic dysfunction [10].It remains unclear if these de nitions are predictive of neonatal outcomes or if other factors (e.g., gestational age at delivery) are equally or even more predictive of adverse outcomes [11,12]. Gestational age (GA) is an established risk factor for poor neonatal and maternal outcomes and the risk of adverse outcomes is negatively associated with increasing GA up to 40 weeks [13,14].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Neonatal Outcomes Using Gestational Age or Diagnosis of Severe Preeclampsia vs. HELLP Syndrome: A Case-Only Analysis

HAUPTMAN,

GILL,

LIM

et al. 2024

Preprint

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Purpose:Hypertensive disorders of pregnancy cause significant neonatal complications. Disease severity is often used to predict neonatal outcomes, however gestational age (GA) at delivery may be a better predictor. We aimed to assess whether disease severity or GA was more predictive of adverse neonatal outcomes. Methods:We included 165 participants with confirmed HELLP syndrome or severe preeclampsia (sPE). Two predictive models were constructed to assess the ability of disease severity compared to GA to predict a composite adverse neonatal outcome. The composite outcome included low birth weight, SGA, IUGR, Apgar score, and neonatal death. Results:Using severity as a predictor of binary neonatal outcome had an AUC of 0.73 (0.65-0.81), with a sensitivity (SE) of 70.3% and a specificity (SP) of 64.4%. For GA, we observed an AUC of 0.82 (0.75-0.89), with a SE of 75.7% and a SP of 76.7%. Conclusion: For the composite neonatal outcome, GA was a better predictor than ACOG diagnosis (severity). This observation underscores the need for further research to validate these findings in larger cohorts and to determine their applicability to maternal outcomes.

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Gestational Age and Pregnancy Outcomes

Cited by 5 publications

References 35 publications

Factors Associated with Preterm Birth and Low Birth Weight in Abu Dhabi, the United Arab Emirates

Factors Associated with Preterm Birth and Low Birth Weight in Abu Dhabi, the United Arab Emirates

Towards physician's experience: Development of machine learning model for the diagnosis of autism spectrum disorders based on complex T‐spherical fuzzy‐weighted zero‐inconsistency method

Prediction of Neonatal Outcomes Using Gestational Age or Diagnosis of Severe Preeclampsia vs. HELLP Syndrome: A Case-Only Analysis

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