首页|Monash University Reports Findings in Preeclampsia (Machine Learning Algorithms Versus Classical Regression Models in Pre- Eclampsia Prediction: A Systematic Rev iew)

Monash University Reports Findings in Preeclampsia (Machine Learning Algorithms Versus Classical Regression Models in Pre- Eclampsia Prediction: A Systematic Rev iew)

扫码查看
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New research on Pregnancy Complications - Preecla mpsia is the subject of a report. According to news reporting out of Melbourne, Australia, by NewsRx editors, research stated, “Machine learning (ML) approaches are an emerging alternative for healthcare risk prediction. We aimed to synthes ise the literature on ML and classical regression studies exploring potential pr ognostic factors and to compare prediction performance for pre-eclampsia.” Financial support for this research came from Monash University. Our news journalists obtained a quote from the research from Monash University, “From 9382 studies retrieved, 82 were included. Sixty-six publications exclusive ly reported eighty-four classical regression models to predict variable timing o f 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, placen tal 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, 9 5% 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. Ca libration performance was not reported in the majority of publications. ML algor ithms had better performance compared to classical regression models in pre-ecla mpsia prediction. Random forest and boosting-type algorithms had the best predic tion performance. Further research should focus on comparing ML algorithms to cl assical regression models using the same samples and evaluation metrics to gain insight into their performance.”

MelbourneAustraliaAustralia and New ZealandAlgorithmsCyborgsEclampsiaEmerging TechnologiesHealth and Medic ineMachine LearningObstetricsPreeclampsiaPregnancy ComplicationsPregna ncy-Induced HypertensionWomen’s Health

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Jun.7)