SynthesisCurrent hypertension reports2024
Machine Learning Algorithms Versus Classical Regression Models in Pre-Eclampsia Prediction: A Systematic Review.
Synthesis in Current hypertension reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Development of a machine learning-based model to predict urethral recurrence following radical cystectomy: a multicentre retrospective study and updated meta-analysis.Scientific reports · 2025Pooled it
- Digital and AI-assisted approaches across the preeclampsia care pathway: a scoping review.AJOG global reports · 2026Article
- Development of antenatal risk prediction model for preeclampsia with severe features in Lagos, Nigeria (PreSev study): protocol of a prospective cohort study.BMC pregnancy and childbirth · 2026Article
- Machine learning models for predicting postpartum convulsions using clinical indicators from PMA Ethiopia data.Scientific reports · 2026Article
- Machine learning-enhanced modeling approach for optimally predicting household level food insecurity in Ethiopia during COVID-19.Scientific reports · 2026Article
- Comparison of Machine Learning Models and the FMF Competing-Risks Algorithm for First-Trimester Preeclampsia Screening in a Romanian Cohort.Diagnostics (Basel, Switzerland) · 2026Article
- Machine learning approaches in identifying factors associated with hypertension and undiagnosed hypertension in adults in rural areas of Bangladesh.Archives of public health = Archives belges de sante publique · 2026Article
- Eclampsia risk prediction across diverse U.S. populations using CDC data: machine learning versus ACOG checklists.AJOG global reports · 2026Article
- Development of immune-derived molecular markers for coronary heart disease via multimachine learning.iScience · 2026Article
- Machine Learning for Dynamic and Short-Term Prediction of Preeclampsia Using Routine Clinical Data.JAMA network open · 2026Article
- Construction and comparison of preeclampsia prediction models using logistic regression and machine learning.BMC medical informatics and decision making · 2026Article
- Developing a prediction model for persistent airflow limitation in asthmatic children.Journal of thoracic disease · 2025Article
- Evaluating the effectiveness of the forest pests and diseases control methods on the industrial wood production using deep learning.Scientific reports · 2025Article
- Development and validation of a predictive model for preeclampsia: a retrospective cohort study.Archives of gynecology and obstetrics · 2025Article
- Exploration of biomarkers for predicting the prognosis of patients with diffuse large B-cell lymphoma by machine-learning analysis.BMC immunology · 2025Article
- Prevention of Pre-Eclampsia: Modern Strategies and the Role of Early Screening.Journal of clinical medicine · 2025Review
- Interpretable machine learning for predicting placental abruption in early-onset preeclampsia: model development and evaluation.Frontiers in medicine · 2025Article
- An explainable machine learning model for predicting preterm birth in pregnant women with gestational diabetes mellitus and hypertensive disorders of pregnancy: development and external validation.Frontiers in endocrinology · 2025Article
- RETRACTED: Interpretable machine learning framework for predicting Urban air quality.PloS one · 2025Article
- Risk Assessment for Preeclampsia in the Preconception Period Based on Maternal Clinical History via Machine Learning Methods.Journal of clinical medicine · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purpose of reviewMachine learning (ML) approaches are an emerging alternative for healthcare risk prediction. We aimed to synthesise the literature on ML and classical regression studies exploring potential prognostic factors and to compare prediction performance for pre-eclampsia. RECENT
findingsFrom 9382 studies retrieved, 82 were included. Sixty-six publications exclusively reported eighty-four classical regression models to predict variable timing of onset of pre-eclampsia. Another six publications reported purely ML algorithms, whilst another 10 publications reported ML algorithms and classical regression models in the same sample with 8 of 10 findings that ML algorithms outperformed classical regression models. The most frequent prognostic factors were age, pre-pregnancy body mass index, chronic medical conditions, parity, prior history of pre-eclampsia, mean arterial pressure, uterine artery pulsatility index, placental growth factor, and pregnancy-associated plasma protein A. Top performing ML algorithms were random forest (area under the curve (AUC) = 0.94, 95% confidence interval (CI) 0.91-0.96) and extreme gradient boosting (AUC = 0.92, 95% CI 0.90-0.94). The competing risk model had similar performance (AUC = 0.92, 95% CI 0.91-0.92) compared with a neural network. Calibration performance was not reported in the majority of publications. ML algorithms had better performance compared to classical regression models in pre-eclampsia prediction. Random forest and boosting-type algorithms had the best prediction performance. Further research should focus on comparing ML algorithms to classical regression models using the same samples and evaluation metrics to gain insight into their performance. External validation of ML algorithms is warranted to gain insights into their generalisability.
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