Comprehensive Approach with Machine Learning Techniques to Investigate Early-Onset Preeclampsia and Its Long-Term Cardiovascular Implications

Preeclampsia (PE), a major cause of perinatal morbidity and mortality, is frequently under-recognized as an early indicator of future cardiovascular (CV) disease. This study examines early-onset preeclampsia (eoPE) across three phases—pre-pregnancy, diagnosis, and follow-up—to dynamically reclassify risk of eoPE in the second-trimester and assess long-term CV implications. A case-control study involving 50 women with eoPE (diagnosed before 34 weeks) and 50 matched controls with uncomplicated pregnancies employed supervised machine learning to develop two predictive models: one for reevaluating first-trimester eoPE risk, with test sensitivity/specificity of 95.0% (92.2–97.8%)/99.0% (97.6–100.0%) and another for predicting post-pregnancy hypertension (HT), with test sensitivity/specificity of 74.1% (67.2–80.9%)/89.1% (85.5–92.8%). Metaheuristic methods identified key features for risk reevaluation and prediction, achieving high predictive performance using routine early pregnancy data and diagnostic information. These findings should be interpreted with caution due to the sample size limitations. Additionally, unsupervised machine learning on follow-up data (median 7.5 years postpartum) was used to explore how pregnancy conditions shape long-term health in eoPE patients.