Risk Assessment for Preeclampsia in the Preconception Period Based on Maternal Clinical History via Machine Learning Methods

Objective: This study was aimed to identify the most effective machine learning (ML) algorithm for predicting preeclampsia based on sociodemographic and obstetric factors during the preconception period. Methods: Data from pregnant women admitted to the obstetric clinic during their first trimester were analyzed, focusing on maternal age, body mass index (BMI), smoking status, history of diabetes mellitus, gestational diabetes mellitus, and mean arterial pressure. The women were grouped by whether they had a preeclampsia diagnosis and by whether they had one or two live births. Predictive models were then developed using five commonly applied ML algorithms. Results: The study included 100 mothers divided into four groups: 22 nulliparous mothers with preeclampsia, 25 nulliparous mothers without preeclampsia, 28 parous mothers with preeclampsia, and 25 parous mothers without preeclampsia. Analysis showed that maternal BMI and family history of diabetes mellitus were the most significant predictive variables. Among the predictive models, the extreme gradient boosting (XGB) classifier demonstrated the highest accuracy, achieving 70% and 72.7% in the respective groups. Conclusions: A predictive model utilizing an ML algorithm based on maternal sociodemographic data and obstetric history could serve as an early detection tool for preeclampsia.