Factors associated with prior pregnancy loss among multigravid women: a retrospective machine learning analysis from an African population cohort

Abstract

Background Pregnancy loss remains a significant challenge in obstetric care, with traditional risk assessment methods, such as clinical history and radiological investigations, often lacking precision in predicting outcomes. This study applied machine learning (ML) to develop a robust model for identifying factors associated with pregnancy loss, with the goal of highlighting candidate predictors for further investigation and validation in future studies in African populations. Methods The study utilized sociodemographic, clinical and laboratory variables collected at baseline from an ongoing cohort study titled “Future maternal cardiovascular health after pre-eclampsia in an indigenous African population”. A total of 1,017 pregnant women attending Mulago National Referral Hospital in Kampala, Uganda were enrolled between 2019 and 2021. However, the outcome question was not administered to women on their first pregnancy, limiting the modeling population to multigravid women (n = 655). Advanced ML tools in R and Python were employed to process the de-identified data, including rigorous data cleaning, feature selection, and handling of missing values. Several ML classifiers were trained and evaluated using cross-validation techniques and the training-test split approach, with performance assessed using Matthew's Correlation Coefficient (MCC), precision, recall and F1 score. Results Random Forest achieved the highest discrimination (MCC 0.36, 95% CI 0.25–0.47; ROC-AUC 0.74, 95% CI 0.63–0.83), followed closely by CatBoost (MCC 0.35, 95% CI 0.18–0.46; ROC-AUC 0.71, 95% CI 0.61–0.77); confidence intervals for the two ensemble models overlapped substantially. Both outperformed logistic regression (MCC 0.22, 95% CI 0.08–0.35). Across both tree-based models and both SHAP and permutation-based importance methods, the number of pregnancies carried past seven months, maternal age, and maternal education ranked as the leading correlates. Subgroup analysis showed broadly consistent discrimination across reliable age, education, and maternal tribe. Conclusion A few sociodemographic and obstetric history factors are statistically associated with a documented history of pregnancy loss among multigravid women in this Ugandan cohort. Because the outcome reflects reproductive history rather than the result of an observed pregnancy, these findings should be read as hypothesis-generating groundwork for future prospective, externally validated risk-prediction research rather than as a clinical tool for identifying women at risk of a future miscarriage.