Machine learning-based evaluation of application value of pulse wave parameter model in the diagnosis of hypertensive disorder in pregnancy
Hypertensive disorder in pregnancy (HDP) remains a major health burden, and it is associated with systemic cardiovascular adaptation. The pulse wave is an important basis for evaluating the status of the human cardiovascular system. This research aims to evaluate the application value of pulse waves...
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AIMS Press
2023-03-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2023363?viewType=HTML |
| _version_ | 1797858249889808384 |
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| author | Xinyu Zhang Yu Meng Mei Jiang Lin Yang Kuixing Zhang Cuiting Lian Ziwei Li |
| author_facet | Xinyu Zhang Yu Meng Mei Jiang Lin Yang Kuixing Zhang Cuiting Lian Ziwei Li |
| author_sort | Xinyu Zhang |
| collection | DOAJ |
| description | Hypertensive disorder in pregnancy (HDP) remains a major health burden, and it is associated with systemic cardiovascular adaptation. The pulse wave is an important basis for evaluating the status of the human cardiovascular system. This research aims to evaluate the application value of pulse waves in the diagnosis of hypertensive disorder in pregnancy.This research a retrospective study of pregnant women who attended prenatal care and labored at Beijing Haidian District Maternal and Child Health Hospital. We extracted maternal hemodynamic factors and measured the pulse wave of the pregnant women. We developed an HDP predictive model by using support vector machine algorithms at five-gestational-week stages.At five-gestational-week stages, the area under the receiver operating characteristic curve (AUC) of the predictive model with pulse wave parameters was higher than that of the predictive model with hemodynamic factors. The AUC values of the predictive model with pulse wave parameters were 0.77 (95% CI 0.64 to 0.9), 0.83 (95% CI 0.77 to 0.9), 0.85 (95% CI 0.81 to 0.9), 0.93 (95% CI 0.9 to 0.96) and 0.88 (95% CI 0.8 to 0.95) at five-gestational-week stages, respectively. Compared to the predictive models with hemodynamic factors, the predictive model with pulse wave parameters had better prediction effects on HDP.Pulse waves had good predictive effects for HDP and provided appropriate guidance and a basis for non-invasive detection of HDP. |
| first_indexed | 2024-04-09T21:10:20Z |
| format | Article |
| id | doaj.art-e9c4f08b27674a099972cfa0e8886e72 |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-04-09T21:10:20Z |
| publishDate | 2023-03-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-e9c4f08b27674a099972cfa0e8886e722023-03-29T01:15:24ZengAIMS PressMathematical Biosciences and Engineering1551-00182023-03-012058308831910.3934/mbe.2023363Machine learning-based evaluation of application value of pulse wave parameter model in the diagnosis of hypertensive disorder in pregnancyXinyu Zhang0Yu Meng 1Mei Jiang2Lin Yang 3Kuixing Zhang4Cuiting Lian5Ziwei Li 61. Faculty of Environment and Life Sciences, Beijing University of Technology, Beijing 100124, China1. Faculty of Environment and Life Sciences, Beijing University of Technology, Beijing 100124, China2. College of Intelligence and Information Engineering, Shandong University of Traditional Chinese Medicine, Jinan 250355, China1. Faculty of Environment and Life Sciences, Beijing University of Technology, Beijing 100124, China2. College of Intelligence and Information Engineering, Shandong University of Traditional Chinese Medicine, Jinan 250355, China1. Faculty of Environment and Life Sciences, Beijing University of Technology, Beijing 100124, China1. Faculty of Environment and Life Sciences, Beijing University of Technology, Beijing 100124, ChinaHypertensive disorder in pregnancy (HDP) remains a major health burden, and it is associated with systemic cardiovascular adaptation. The pulse wave is an important basis for evaluating the status of the human cardiovascular system. This research aims to evaluate the application value of pulse waves in the diagnosis of hypertensive disorder in pregnancy.This research a retrospective study of pregnant women who attended prenatal care and labored at Beijing Haidian District Maternal and Child Health Hospital. We extracted maternal hemodynamic factors and measured the pulse wave of the pregnant women. We developed an HDP predictive model by using support vector machine algorithms at five-gestational-week stages.At five-gestational-week stages, the area under the receiver operating characteristic curve (AUC) of the predictive model with pulse wave parameters was higher than that of the predictive model with hemodynamic factors. The AUC values of the predictive model with pulse wave parameters were 0.77 (95% CI 0.64 to 0.9), 0.83 (95% CI 0.77 to 0.9), 0.85 (95% CI 0.81 to 0.9), 0.93 (95% CI 0.9 to 0.96) and 0.88 (95% CI 0.8 to 0.95) at five-gestational-week stages, respectively. Compared to the predictive models with hemodynamic factors, the predictive model with pulse wave parameters had better prediction effects on HDP.Pulse waves had good predictive effects for HDP and provided appropriate guidance and a basis for non-invasive detection of HDP.https://www.aimspress.com/article/doi/10.3934/mbe.2023363?viewType=HTMLhypertensive disorders in pregnancypulse wave parametershemodynamic factorssupport vector machinepredictive model |
| spellingShingle | Xinyu Zhang Yu Meng Mei Jiang Lin Yang Kuixing Zhang Cuiting Lian Ziwei Li Machine learning-based evaluation of application value of pulse wave parameter model in the diagnosis of hypertensive disorder in pregnancy Mathematical Biosciences and Engineering hypertensive disorders in pregnancy pulse wave parameters hemodynamic factors support vector machine predictive model |
| title | Machine learning-based evaluation of application value of pulse wave parameter model in the diagnosis of hypertensive disorder in pregnancy |
| title_full | Machine learning-based evaluation of application value of pulse wave parameter model in the diagnosis of hypertensive disorder in pregnancy |
| title_fullStr | Machine learning-based evaluation of application value of pulse wave parameter model in the diagnosis of hypertensive disorder in pregnancy |
| title_full_unstemmed | Machine learning-based evaluation of application value of pulse wave parameter model in the diagnosis of hypertensive disorder in pregnancy |
| title_short | Machine learning-based evaluation of application value of pulse wave parameter model in the diagnosis of hypertensive disorder in pregnancy |
| title_sort | machine learning based evaluation of application value of pulse wave parameter model in the diagnosis of hypertensive disorder in pregnancy |
| topic | hypertensive disorders in pregnancy pulse wave parameters hemodynamic factors support vector machine predictive model |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2023363?viewType=HTML |
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