Using ICU Admission as a Predictor for Maternal Mortality: Identifying Essential Features for Accurate Classification

Maternal mortality (MM) is a pressing global health issue that results in thousands of mothers dying annually from pregnancy-related complications. Despite spending trillions of dollars on the healthcare industry, the U.S. continues to experience one of the highest rates of maternal death (MD) compared to other developed countries. This ongoing public health crisis highlights the urgent need for innovative strategies to detect and mitigate adverse maternal outcomes. This study introduces a novel approach, utilizing admission to the ICU as a proxy for MM. By analyzing 14 years of natality birth data, this study aims to explore the complex web of factors that elevate the chances of MD. The primary goal of this study is to identify features that are most influential in predicting ICU admission cases. These factors hold the potential to be applied to MM, as they can serve as early warning signs that complications may arise, allowing healthcare professionals to step in and intervene before adverse maternal outcomes occur. Two supervised machine learning models were employed in this study, specifically Logistic Regression (LR) and eXtreme Gradient Boosting (XGBoost). The models were executed twice for each dataset: once incorporating all available features and again utilizing only the most significant features. Following model training, XGBoost’s feature selection technique was employed to identify the top 10 influential features that are most important to the classification process. Our analysis revealed a diverse range of factors that are important for the prediction of ICU admission cases. In this study, we identified maternal transfusion, labor and delivery characteristics, delivery methods, gestational age, maternal attributes, and newborn conditions as the most influential factors to categorize maternal ICU admission cases. In terms of model performance, the XGBoost consistently outperformed LR across various datasets, demonstrating higher accuracy, precision, and F1 scores. For recall, however, LR maintained higher scores, surpassing those of XGBoost. Moreover, the models consistently achieved higher scores when trained with all available features compared to those trained solely with the top features. Although the models demonstrated satisfactory performance in some evaluation metrics, there were notable deficiencies in recall and precision, which suggests further model refinement is needed to effectively predict these cases.