ArticleTechnology and health care : official journal of the European Society for Engineering and Medicine2020
Predictive models of hypertensive disorders in pregnancy based on support vector machine algorithm.
Article in Technology and health care : official journal of the European Society for Engineering and Medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 16 citations in OpenAlex.
- Identifying Predictor Variables for a Composite Risk Prediction Tool for Gestational Diabetes and Hypertensive Disorders of Pregnancy: A Modified Delphi Study.Healthcare (Basel, Switzerland) · 2024Article
- Use of Machine Learning for Early Detection of Maternal Cardiovascular Conditions: Retrospective Study Using Electronic Health Record Data.JMIR cardio · 2024Article
- The association of a panel of circulating markers with hypertensive disorders of pregnancy: the jury is still out.Hypertension research : official journal of the Japanese Society of Hypertension · 2023Article
- Cuffless Hypertension Detection using Swarm Support Vector Machine Utilizing Photoplethysmogram and Electrocardiogram.Journal of biomedical physics & engineering · 2023Article
- Artificial intelligence in obstetrics.Obstetrics & gynecology science · 2022Article
- Optimising Cardiometabolic Risk Factors in Pregnancy: A Review of Risk Prediction Models Targeting Gestational Diabetes and Hypertensive Disorders.Journal of cardiovascular development and disease · 2022Review
- Design and Implementation of Financial Service and Management Platform considering Support Vector Machine Algorithm.Computational intelligence and neuroscience · 2022Article
- Comparison of machine learning and logistic regression as predictive models for adverse maternal and neonatal outcomes of preeclampsia: A retrospective study.Frontiers in cardiovascular medicine · 2022Article
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe risk factors of hypertensive disorders in pregnancy (HDP) could be summarized into three categories: clinical epidemiological factors, hemodynamic factors and biochemical factors.
objectiveTo establish models for early prediction and intervention of HDP.
methodsThis study used the three types of risk factors and support vector machine (SVM) to establish prediction models of HDP at different gestational weeks.
resultsThe 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%.
conclusionMulti-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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.