Early Prediction of Preeclampsia Using Ensemble Machine Learning With Multi-Method Explainable AI Approach

Preeclampsia is a pregnancy complication that endangers the mother and fetus. Early detection is necessary to prevent serious complications. Here, the authors employ mixed methods, which combine quantitative and qualitative approaches to design a preeclampsia risk prediction system for pregnant women. The system uses four machine learning algorithms: logistic regres-sion, decision tree, support vector machine (SVM), and random forest (RF). We conducted the evaluation process by using a multi-method explainable AI (XAI) approach with Shapley additive explanations, local interpretable model-agnostic expla-nations, and permutation feature importance to enhance the transparency and ease of  interpretation of  the results. Clinical variables included systolic and diastolic blood pressure, blood glucose, body temperature, heart rate, age, and urine protein. The results show that RF and SVM achieved the highest accuracy (78%) with relatively stable performance across risk cate-gories. Multi-method XAI analysis indicated that blood pressure and blood glucose frequently appeared among influential features, although their relative importance varied depending on the model and explainability method. However, due to the limited dataset size and use of  internal validation only, these findings should be interpreted as preliminary and multi-method XAI  early  identification  of   preeclampsia  risk  factors,  not  to  replace  clinical  diagnosis  or  function  as  a  standalone  clinical  decision-making tool.