Predictive models of hypertensive disorders in pregnancy based on support vector machine algorithm.

Background: The risk factors of hypertensive disorders in pregnancy (HDP) could be summarized into three categories: clinical epidemiological factors, hemodynamic factors and biochemical factors.

Objective: To establish models for early prediction and intervention of HDP.

Methods: This study used the three types of risk factors and support vector machine (SVM) to establish prediction models of HDP at different gestational weeks.

Results: The average accuracy of the model was gradually increased when the pregnancy progressed, especially in the late pregnancy 28-34 weeks and ⩾ 35 weeks, it reached more than 92%.

Conclusion: Multi-risk factors combined with dynamic gestational weeks' prediction of HDP based on machine learning was superior to static and single-class conventional prediction methods. Multiple continuous tests could be performed from early pregnancy to late pregnancy.