Prediction Models for Late-Onset Preeclampsia: A Study Based on Logistic Regression, Support Vector Machine, and Extreme Gradient Boosting Models

<b>Background:</b> Preeclampsia, affecting 2–4% of pregnancies worldwide, poses a substantial risk to maternal health. Late-onset preeclampsia, in particular, has a high incidence among preeclampsia cases. However, existing prediction models are limited in terms of the early detection ca...

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Bibliographic Details
Main Authors:	Yangyang Zhang, Xunke Gu, Nan Yang, Yuting Xue, Lijuan Ma, Yongqing Wang, Hua Zhang, Keke Jia
Format:	Article
Language:	English
Published:	MDPI AG 2025-02-01
Series:	Biomedicines
Subjects:	late-onset preeclampsia maternal risk factors laboratory indicators logistic regression support vector machine extreme gradient boosting
Online Access:	https://www.mdpi.com/2227-9059/13/2/347
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Prediction Models for Late-Onset Preeclampsia: A Study Based on Logistic Regression, Support Vector Machine, and Extreme Gradient Boosting Models

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