Identifying the Early Signs of Preterm Birth from U.S. Birth Records Using Machine Learning Techniques

Abstract

Preterm birth (PTB) is the leading cause of infant mortality in the U.S. and globally. The goal of this study is to increase understanding of PTB risk factors that are present early in pregnancy by leveraging statistical and machine learning (ML) techniques on big data. The 2016 U.S. birth records were obtained and combined with two other area-level datasets, the Area Health Resources File and the County Health Ranking. Then, we applied logistic regression with elastic net regularization, random forest, and gradient boosting machines to study a cohort of 3.6 million singleton deliveries to identify generalizable PTB risk factors. The response variable is preterm birth, which includes spontaneous and indicated PTB, and we performed a binary classification. Our results show that the most important predictors of preterm birth are gestational and chronic hypertension, interval since last live birth, and history of a previous preterm birth, which explains 10.92, 5.98, and 5.63% of the predictive power, respectively. Parents' education is one of the influential variables in predicting PTB, explaining 7.89% of the predictive power. The relative importance of race declines when parents are more educated or have received adequate prenatal care. The gradient boosting machines outperformed with an AUC of 0.75 (sensitivity: 0.64, specificity: 0.73) for the validation dataset. In this study, we compare our results with seminal and most related studies to demonstrate the superiority of our results. The application of ML techniques improved the performance measures in the prediction of preterm birth. The results emphasize the importance of socioeconomic factors such as parental education as one of the most important indicators of preterm birth. More research is needed on these mechanisms through which socioeconomic factors affect biological responses.

Data Science in Health Services

“…Two of the articles are explicitly clinical, focusing on care for individual patients. Ebrahimvandi et al [2] apply several machine learning algorithms to a dataset of 3.6 million deliveries to predict preterm births, achieving greater accuracy than in similar studies. The article also identifies several clinical and social factors that help to predict preterm births and assessed their independent and interactive effects.…”

“…Together, these articles demonstrate the breadth of data science uses in health services research. In many ways, some data science applications are natural extensions of systematic case collection and pattern analysis established as good practice centuries ago, albeit with a much larger sample of cases [2,5]. But data science also enhances the effectiveness of health systems and services by supporting more precise surgery [3], monitoring of health promotion programs [4], and even the way in which we think about the complexity of health systems [6].…”