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

1.Introduction

Hypertensive disorders in pregnancy (HDP) has been a major cause of increased morbidity and mortality among pregnant women and perinatal infants [1]. There are various risk factors of HDP, mainly related to clinical epidemiology, hemodynamics and biochemistry [2, 3, 4, 5]. Using epidemiological factors alone to predict HDP, the detection rate was only about 30%. Studies have shown that maternal factors and uterine artery Doppler were better than maternal factors alone in predicting early-onset preeclampsia [6]. A joint assessment of multiple risk factors is needed. In order to improve the prediction rate, researchers have carried out a variety of combinations of different biomarkers. Myers et al. used maternal historical medical information and biochemical information placental growth factor (PlGF), soluble endoglin (sEng) and soluble fms-like tyrosine kinase-1 (sFlt-1) to establish a joint prediction model. The sensitivity of the model to predict pre-eclampsia was 45% at 14–16 weeks of gestation when the false positive rate was 5% [7]. Jaana et al. also used maternal characteristics and combined screening markers to develop a predictive risk model for early-onset pre-eclampsia. The best detection rate was 47% [8].

Current studies have focused on exploring changes in static parameters at a specific time during the first and second trimesters of pregnancy, and prospectively assessing the risk of HDP [9, 10, 11, 12]. However, all kinds of static and single-class conventional prediction methods did not achieve very good model accuracy and effect. Some prediction models had relatively high detection rates for early-onset preeclampsia. Nevertheless, the detection rate was not ideal for late-onset preeclampsia or gestational hypertension.

With the extensive establishment of the hospital database, data mining and machine learning are gradually applied to clinical research. HDP prediction models at different gestational weeks could be established by effectively applying hospital medical record data and combining machine learning algorithms.

2.Materials and methods

3.Results

The total number of pregnant women included in this study was 690, who were divided into the HDP group (300 women) and normal group (390 women). Detection times were randomly selected for each woman during the different gestational weeks, as can be seen in Table 3.

There are two batches of data in the data collection process. Five hundred and seven subjects gave birth at Beijing Obstetrics and Gynecology Hospital from 2006 to 2008 and 183 subjects gave birth at Beijing Haidian Maternal and Children Health Hospital from 2015 to 2016. The ratio was 3 to 1. In Table 4, the training data set which belonged to the first batch of data were cross-validated five times by combining three kinds of risk factors. As shown in Table 4, the predictive value of these factors of HDP was explored.

As shown in Table 4, with the increase of risk factors of HDP, the predictive effect improved. In the five sub-models provided in Table 1, 5-fold cross-validation of the training sets which randomly accounted for 75% of the total number of each sub-model were carried out respectively (Table 5).

As shown in Table 5, the average accuracy of the model gradually increased when the pregnancy progressed, and the overall accuracy was more than 80%. Especially in late pregnancy, the average accuracy of 28-34 weeks and ⩾ 35 weeks models could reach more than 92%. The highest average accuracy of the model without considering gestational weeks was only 87.64% (Table 4).

4.Discussion

Nicolaides et al. established risk models based on maternal factors, uterine artery pulsation index, and more at 11–13 weeks, 19–24 weeks, 30–34 weeks and 35–37 weeks [15, 16, 17, 18]. Lim et al. recorded the blood vessel activity factor and uterine artery pulsation index of pregnant women in the four stages of 11–14, 18–22, 28–32 and 34 weeks of pregnancy, and measured the central artery pressure of pregnant women by radial artery pulse wave to explore the changes of arteriosclerosis [19]. Gómez et al. tracked the uterine artery pulsation index at 11–41 weeks [20]. This study aimed to establish a reference range of pulsation index based on gestational weeks. We furthermore aimed to accurately assess the risk of HDP, which required comprehensive measurements of multiple risk factors for pregnant women at all gestational weeks.

In this study, support vector machine (SVM) was used to establish the predictive model of HDP. Combined prediction of epidemiology, hemodynamics and biochemistry was helpful to improve the model accuracy, and the discriminant effect was better than that of single prediction. The prediction accuracy of the model considering gestational weeks increased gradually with the progress of pregnancy, which was better than the model without considering this factor.

5.Conclusion

Based on the literature, the risk factors of HDP are summarized as clinical epidemiological factors, hemodynamic factors and biochemical factors. The dynamic risk assessment model of HDP by SVM was established with the change of gestational weeks. Continuous testing could be carried out from early to late pregnancy for early detection of risk and intervention. The predictive model proposed in this study could be applied to the risk assessment of all kinds of HDP. In future studies, the model needs to be refined according to different diseases.