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

Abstract

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.

Contribution of Artificial Intelligence in Pregnancy: A Scoping Review

“…The majority of the studies (n=22) were published between 2016 and 2020. AI techniques were used for predicting pregnancy disorders/complications in about 75% (n=18) of the included studies [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21]. Specifically, the techniques discussed were utilized for predicting preeclampsia [5,7,9,15], preterm birth [6,13,19], gestational diabetes [8,14,21], gestational age [4,18], patient's metabolomics profile [12,20], suicidal behavior [11], uterine contractions [16], labor due date [17], and hypertensive disorder [10].…”

“…AI techniques were used for predicting pregnancy disorders/complications in about 75% (n=18) of the included studies [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21]. Specifically, the techniques discussed were utilized for predicting preeclampsia [5,7,9,15], preterm birth [6,13,19], gestational diabetes [8,14,21], gestational age [4,18], patient's metabolomics profile [12,20], suicidal behavior [11], uterine contractions [16], labor due date [17], and hypertensive disorder [10]. Additionally, of the 24 studies, four (n=4, 17%) employed AI techniques for treatment and management of ectopic pregnancies [22], gestational diabetes [23], late-onset preeclampsia [5], and hypertensive disorder [10].…”

“…Specifically, the techniques discussed were utilized for predicting preeclampsia [5,7,9,15], preterm birth [6,13,19], gestational diabetes [8,14,21], gestational age [4,18], patient's metabolomics profile [12,20], suicidal behavior [11], uterine contractions [16], labor due date [17], and hypertensive disorder [10]. Additionally, of the 24 studies, four (n=4, 17%) employed AI techniques for treatment and management of ectopic pregnancies [22], gestational diabetes [23], late-onset preeclampsia [5], and hypertensive disorder [10]. Further, five studies (n= 5, 21%) used AI to assist with patients' safety outcome [13,19,[24][25][26].…”

Optimising Cardiometabolic Risk Factors in Pregnancy: A Review of Risk Prediction Models Targeting Gestational Diabetes and Hypertensive Disorders

“…Meanwhile, in a Korean study of over 11,000 women, Jhee et al showed that the combined application of maternal factors and common antenatal laboratory data in various machine learning algorithms resulted in improved prediction performance of PE, compared to traditional statistical models [78]. In a smaller study of Chinese women, support vector machine algorithms based on a combination of epidemiological, haemodynamic and biochemical factors were found to improve model accuracy and discrimination of HDP [80].…”

Artificial intelligence in obstetrics

“…Finally, there is an emerging body of literature on the application of machine learning with gestational diabetes or hypertension as the dependent variable [71][72][73][74]. The independent variables of machine-learning studies on gestational diabetes [71,72] include age, blood pressure, body mass index, diabetes pedigree function, education (elementary, junior high school, senior high school, and university), fasting plasma glucose, gestational diabetes history, pregnancies (number), serum insulin, skin-fold thickness, and smoking.…”