Prediction model of preeclampsia using single nucleotide polymorphism and machine learning methods: A population-based cohort study in Jordan

Preeclampsia is the most common medical disorder in pregnancy that is steadily increasing over time. Evidence indicates that early identification of women at high risk, followed by timely preventive interventions such as Aspirin and close surveillance, can significantly reduce disease incidence and severity, highlighting the importance of accurate early prediction. At the molecular level, preeclampsia is primarily a placental disorder associated with dysregulated microRNA (miRNA) expression. DICER, a key enzyme in miRNA biogenesis, and its single nucleotide polymorphisms (SNPs) have been shown to influence placental miRNA profiles and disease susceptibility. In this study, artificial intelligence-driven data mining and machine learning classifiers integrating genetic (DICER SNPs) and clinical features were developed and evaluated in a sample of Jordanian population. Six different classifiers were applied to predict the occurrence of preeclampsia, stratify disease severity, and distinguish early- from late-onset disease. This study included 224 pregnant women, with 137 controls and 87 women who had developed preeclampsia. Genotypes of rs3742330 and rs14035 SNPs in DICER and RAN genes respectively, were determined using polymerase chain reaction techniques, and clinical data was recruited. Six machine learning classifiers were used to build the prediction models. Model performance was estimated using repeated stratified k-fold cross-validation to provide robust, unbiased estimates given the limited sample size. The results showed that ensemble methods Random Forest and XGBoost outperformed other models with the highest accuracy, sensitivity, precision, F1-scores, and G-means. For occurrence prediction, Random Forest achieved a cross-validated accuracy of 0.973 ± 0.040, sensitivity of 0.953 ± 0.058 and F1-score of 0.965 ± 0.050, while XGBoost attained an accuracy of 0.978 ± 0.022, indicating that combining genetic with clinical factors provides strong discriminative ability compared to models relying solely on traditional risk factors. Thus, integrating genetic information, particularly the DICER rs3742330 polymorphism, with routine early pregnancy clinical features using data mining and machine learning techniques yields strong predictive performance for preeclampsia in the Jordanian population. These findings suggest that such AI-driven models could be translated into practical clinical applications to support early risk stratification.