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A comprehensive first-trimester predictive model for preeclampsia based on multi-indicators and machine learning: A

Haixia Liang1, Xuejing Zhao, Ying Zhang

  • 1Department of Obstetrics and Gynecology, Xijing Hospital the 986th Hospital Department, The Fourth Military Medical University, Xi'an, Shaanxi, China.

Medicine
|November 27, 2025
PubMed
Summary

This study developed a predictive model for preeclampsia (PE) using early pregnancy biomarkers and machine learning. The neural network model achieved high accuracy in predicting PE, aiding early intervention for better maternal and fetal outcomes.

Keywords:
early pregnancyinflammationmachine learningplacental growth factorpredictive modelpreeclampsiauterine artery pulsatility index

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Area of Science:

  • Obstetrics and Gynecology
  • Maternal-Fetal Medicine
  • Biomedical Data Science

Background:

  • Preeclampsia (PE) is a severe pregnancy disorder causing significant maternal and perinatal complications.
  • Early prediction of PE is crucial for timely intervention and improved outcomes.
  • Current prediction methods lack comprehensive early-stage assessment.

Purpose of the Study:

  • To develop and validate a predictive model for preeclampsia using first-trimester maternal indicators.
  • To identify key biomarkers and employ machine learning for robust PE prediction.
  • To assess the model's generalizability in an independent external validation cohort.

Main Methods:

  • Retrospective study of 100 pregnant individuals (50 PE, 50 controls) with first-trimester data.
  • Feature selection using Least Absolute Shrinkage and Selection Operator (LASSO) regression.
  • Development and evaluation of 7 machine learning algorithms, including a neural network model, with external validation.

Main Results:

  • LASSO identified 12 key predictive features, including placental growth factor (PlGF), uterine artery pulsatility index (UtAPI), C-reactive protein (CRP), and neutrophil-to-lymphocyte ratio (NLR).
  • The neural network model achieved an area under the curve (AUC) of 0.917 in internal validation and 0.838 in external validation.
  • SHapley Additive exPlanations confirmed PlGF, UtAPI, CRP, and NLR as highly influential predictors.

Conclusions:

  • A robust predictive model for preeclampsia was developed using early pregnancy biomarkers and machine learning.
  • The neural network model demonstrates superior discriminative ability for PE prediction.
  • Early identification via this model can facilitate timely interventions, potentially improving maternal and fetal health.