Predicting Low Birth Weight Babies Through Data Mining

Loreto, Patrícia; Peixoto, Hugo; Abelha, António; Machado, José

doi:10.1007/978-3-030-16187-3_55

Cited by 13 publications

(14 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this category, studies mainly presented ML models for: prediction of birth outcomes (term birth weight, small for gestational age, preterm birth, and low 5 min Apgar score) in [33], prediction of fetal weight in [109], prediction of large for gestational age (LGA) or small for gestational age (SGA) in [110]- [112], prediction of fetal growth abnormalities in [113], prediction of birth weight in [114], prediction of low birth weight (LBW) in [54] and [115], measurement of head circumference (HC) in [116], prediction of fetal HC in [117], and evaluation of the relationship existing between pollutants exposure and low weight at birth in [118].…”

Section: F Fetal Growthmentioning

confidence: 99%

“…The vast majority of studies reported using ML techniques, except for [54], in which DM techniques were developed. 75% (n = 9) of studies implemented classification tasks, meanwhile regression was reported in one study [114].…”

Section: F Fetal Growthmentioning

confidence: 99%

“…The majority of studies reported data preparation processes. In [54], [113]- [115] and [118], missing or incomplete data was excluded, meanwhile in [112], missing values were replaced with the median of the rest of the observed values. Feature selection techniques were implemented in [110], [111], [115] and [116].…”

Section: F Fetal Growthmentioning

confidence: 99%

See 2 more Smart Citations

Artificial Intelligence in Pregnancy: A Scoping Review

et al. 2020

View full text Add to dashboard Cite

Artificial Intelligence has been widely applied to a majority of research areas, including health and medicine. Certain complications or disorders that can appear during pregnancy can endanger the life of both mother and fetus. There is enough scientific literature to support the idea that emotional aspects can be a relevant risk factor in pregnancy (such as anxiety, stress or depression, for instance). This paper presents a scoping review of the scientific literature from the past 12 years (2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018)(2019)(2020) to identify which methodologies, techniques, algorithms and frameworks are used in Artificial Intelligence and Affective Computing for pregnancy health and well-being. The methodology proposed by Arksey and O'Malley, in conjunction with PRISMA-ScR framework has been used to create this review. Despite the relevance that emotional status can have as a risk factor during pregnancy, one of the main findings of this study is that there is still not a significant amount of literature on automatic analysis of emotion. Health enhancement and well-being for pregnant women can be achieved with artificial intelligence or affective computing based devices, hence future work on this topic is strongly suggested.

show abstract

Section: F Fetal Growthmentioning

confidence: 99%

Section: F Fetal Growthmentioning

confidence: 99%

See 1 more Smart Citation

Artificial Intelligence in Pregnancy: A Scoping Review

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The authors collected a dataset of 96 pregnant women that contained a maternal questionnaire and a detailed evaluation by three clinicians from RadyoEmar Imaging Center, a medical diagnostic imaging center in Istanbul, Turkey. Loreto et al 15 evaluated the performance of six ML algorithms for LBW classification, i.e., RF, adaptive boosting (AdaBoost), NB, KNN, SVM, and DT. A dataset of 2,328 instances was used.…”

Section: Related Workmentioning

confidence: 99%

“…The primary contributions of this paper are as follows. We proposed a self-created dataset that contained features similar to those used by Hussain et al 11 , Faruk et al 12 , Kuhle et al 13 , Senthilkumar et al 14 , Loreto et al 15 , and Kader et al 16 . The created dataset contained 88 features, including infant BW as a target label.…”

Section: Introductionmentioning

confidence: 99%

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

Khan

Masud

Zaki

et al. 2022

Sci Rep

View full text Add to dashboard Cite

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.

show abstract