Debiasing for trustworthy AI-based prediction of preterm birth

Objective Preterm birth (PTB), defined as delivery before 37 weeks of gestation, is the leading cause of perinatal morbidity and mortality. Timely prediction of PTB is crucial for physicians to take preventive action. In this work, we propose an AI-based clinical decision support system to address this challenge. Methods We use an existing dataset containing demographic and clinical variables from 973 pregnant women and begin by examining it for potential data-level bias and representativeness issues. Upon identifying an outcome prevalence imbalance in one of the variables, we revised the dataset through a preprocessing adjustment. We then applied several Machine Learning (ML) algorithms to predict PTB as a binary outcome. Results During internal model evaluation we found that an optimized ensemble (voting) of Logistic Regression and XGBoost performed best, achieving an accuracy of 96% and a recall of 98%. We also compared the predictive performance using the original (biased) and revised (de-biased) datasets, observing statistically significant improvements in the revised dataset as confirmed by the Wilcoxon signed-rank test, with p-values less than 0.05 for Accuracy, Recall, and F1-score. Conclusion The results highlight the importance of data quality and dataset representativeness in developing reliable and trustworthy AI-based applications for PTB prediction.