Early and Accurate Diagnosis of Ectopic Pregnancy: A SHAP-Based Explainable Machine Learning Approach

Ectopic pregnancy (EP) is a significant cause of maternal morbidity and mortality, particularly in the first trimester. It occurs when a fertilized ovum implants outside the uterine cavity, with the fallopian tubes being the most common location. Early and accurate diagnosis is critical to prevent complications such as tubal rupture and hemorrhage. Traditional diagnostic methods rely on transvaginal ultrasound (TVUSG) and serial β -hCG measurements, but these approaches can sometimes yield inconclusive results. Recent progress in artificial intelligence has demonstrated potential for improving the precision of ectopic pregnancy detection. This study examines the use of artificial intelligence-based classification models to predict pregnancy outcomes using routinely available clinical data. The dataset comprises 310 patient records collected from real-world clinical practice. Several classification approaches were evaluated, with particular emphasis on tree-based ensemble models. Experimental results indicate that the LightGBM DART model outperformed the other evaluated classifiers, achieving the highest overall performance with an accuracy of 0.9161, a macro-AUC of 0.9409, and a balanced accuracy of 0.9080, followed by CatBoost and Histogram-based Gradient Boosting models. To enhance model transparency and clinical interpretability, Shapley Additive Explanations (SHAP) were applied to identify the most influential features contributing to model predictions. These findings suggest that explainable artificial intelligence (XAI)-based predictive models may support early and accurate diagnosis during the early follow-up period (Day 0-12), assist clinicians in timely decision-making, and potentially improve patient management, rather than serving as a standalone tool for initial Day-0 triage.