In Technology and health care : official journal of the European Society for Engineering and Medicine
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.
Yang Lin, Sun Ge, Wang Anran, Jiang Hongqing, Zhang Song, Yang Yimin, Li Xuwen, Hao Dongmei, Xu Mingzhou, Shao Jing
Support vector machine algorithm, machine learning, model research