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other·obstetrics and gynecology, pediatrics, public health, clinical trial, research methods·PMC10267797

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

Journal of Medical Internet Research · 5 authors, 3 centres

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TL;DR

This study established benchmark machine learning models to predict low birthweight from a large, imbalanced dataset of U.S. birth records, finding that extreme gradient boosting with weight rebalancing achieved the best performance and that maternal race, age, and payment source were key risk factors.

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To address class imbalance, four rebalancing methods were tested. Feature importance analysis via SHAP values identified key risk factors for LBW, including non-Hispanic Black race, adolescent or advanced maternal age, Medicaid payment source, high predelivery hospitalizations, and specific social vulnerability index components. The findings provide benchmarks for ML in maternal health and identify modifiable risk factors that could inform targeted interventions and policy.

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other

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obstetrics and gynecology

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ORIGINAL PAPER

PMC10267797

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

Journal of Medical Internet Research

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ABSTRACT

Abstract 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. Keywords: low birthweight, machine learning, risk factor, benchmark, data rebalance

CITATION EXPORT

Yang Ren, Dezhi Wu, Yan Tong et al.. 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. Journal of Medical Internet Research. (2023). https://doi.org/10.2196/44081

AUTHORS

5 · 3 CENTRES

Yang Ren, Dezhi Wu, Yan Tong, Ana López-DeFede, Sarah Gareau

1Department of Computer Science, University of South Carolina, Columbia, SC, United States
2Department of Integrated Information Technology, University of South Carolina, Columbia, SC, United States
3The Institute of Families in Society, University of South Carolina, Columbia, SC, United States