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Predictive models of hypertensive disorders in pregnancy based on support vector machine algorithm
Lin Yang1,1, Ge Sun1,1, Anran Wang2,1
1College of Life Science and Bioengineering, Beijing University of Technology, Intelligent Physiological Measurement and Clinical Translation, Beijing International Base for Scientific and Technological Cooperation, Beijing, 100024, China.
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.
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