Analysis of risk factors and early prediction model construction for gestational hypertension

Gestational hypertension (GH), a prevalent pregnancy complication, requires early risk identification for timely intervention. This study assesses and compares traditional and placental function factors using multivariable logistic regression, random forest, and support vector machine (SVM) models to predict GH risk. We first compared the baseline information and pregnancy-related characteristics between normal pregnant women and those with GH. Then, we modeled the risk of GH based on traditional factors and placental function factors using multivariable logistic regression, random forest, and SVM combined with SHapley Additive exPlanations values. The predictive performance of each model was assessed using receiver operating characteristic curves. Among the models compared, the multivariable logistic regression model based on traditional factors achieved the highest area under the curve (AUC), demonstrating the best predictive performance. The AUC values for random forest and SVM using traditional factors were 0.730 and 0.732, respectively, but their performance was weaker when using placental function factors, with random forest having the lowest AUC (0.612). Feature importance analysis indicated that baseline systolic blood pressure, diastolic blood pressure, high-risk pregnancy, and family history were key predictive factors among traditional factors, while fasting plasma glucose, triglycerides, and C-reactive protein were the most important among placental function factors. Traditional factors best predicted GH, with logistic regression outperforming machine learning methods. While SVM and random forest showed moderate performance with traditional factors, they were less effective with placental function factors. Logistic regression should remain primary, supplemented by other methods for comprehensive prediction.