Naive Bayes classifier for infant weight prediction of hypertension mother

Desiani, Anita; Primartha, Rifkie; Arhami, Muhammad; Orsalan, Osvari

doi:10.1088/1742-6596/1282/1/012005

Cited by 16 publications

(12 citation statements)

References 26 publications

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“…Linear and non-linear regression analysis for prediction of neonatal weight at birth is presented in 12 . Several such research has been carried out to predict infant weight using probabilistic models 13 , nutritional prediction using artificial intelligence 14 , birth weight prediction using neural network 15,16 , dog body weight prediction using morphological parameters 17 . Other than data mining, image processing-based body weight prediction also proved efficient 18 .…”

Section: Based Body Weight Predictionmentioning

confidence: 99%

Impact of Education and Socio-Economic Status on Post-Natal Body Weight using Machine Learning Approach

Saishree Sahu,

Prasanna Kumar Dixit,

Chinmayee Dora

2023

IJPHRD

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Background:The maternal education and socio-economic status of the mother greatly affect the growth of the baby. Nutritional deficiency is the major cause of motor and physiological disorders in children. Hence, predicting body weight could be used to monitor the growth of the babies. Although the prediction of weights is standardized by World Health Organization (WHO), for babies based on their gender and age; generalizing it for all locations and socio-economic variations is not possible Method: Hence, in this paper, an ANN-based predictive approach to establish the relationship between the factors like socioeconomic status, age, and prior weights to predict the baby's weight using a locally acquired dataset. This could be helpful for the health workers to properly diagnose the disease a time ahead. Conclusion:The proposed work suggests predicting the baby's growth rate using Machine Learning (ML) techniques is both an efficient and feasible approach.

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Section: Based Body Weight Predictionmentioning

confidence: 99%

Impact of Education and Socio-Economic Status on Post-Natal Body Weight using Machine Learning Approach

Saishree Sahu,

Prasanna Kumar Dixit,

Chinmayee Dora

2023

IJPHRD

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“…The data were obtained from 600 pregnant women; however, only 9.5% of the cases were LBW. Desiani et al 1 applied an NB classifier to maternal data for predicting the weight of infants delivered by hypertensive and nonhypertensive mothers. Their dataset included the data of 219 patients from Muhammadiyah Hospital Palembang in Indonesia.…”

Section: Related Workmentioning

confidence: 99%

“…Low BW in infants can occur because of various reasons such as maternal diet, close pregnancy intervals, infections, high parity, preterm delivery, and socioeconomic factors. Compared with normal BW infants, LBW infants are at a higher risk of perinatal death at a ratio of 8:1 1 . Moreover, LBW infants have a greater chance of having serious development problems such as low intelligence quotient (IQ), mental retardation, visual and hearing impairment, neonatal hypothermia, neonatal hypoglycemia, long-term disabilities, and premature death 2 , 3 .…”

Section: Introductionmentioning

confidence: 99%

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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

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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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“…Table 1 summarizes the reviewed literature into 3 categories based on their data size and imbalance issues. The first group of studies used a small data set consisting of <1000 records [ 20 , 21 , 25 , 26 ]. In this group, 50% (2/4) of these studies reported the percentage of LBW cases, which highlights the presence of data imbalance issues [ 20 , 21 ].…”

Section: Introductionmentioning

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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Naive Bayes classifier for infant weight prediction of hypertension mother

Cited by 16 publications

References 26 publications

Impact of Education and Socio-Economic Status on Post-Natal Body Weight using Machine Learning Approach

Impact of Education and Socio-Economic Status on Post-Natal Body Weight using Machine Learning Approach

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

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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