Machine Learning Applications in Preeclampsia Prediction

Machine learning is transforming the early identification of preeclampsia by enabling automated analysis of complex clinical and biological data. Traditional risk assessment relies on demographic and physiological measurements, but machine learning models integrate diverse inputs—ranging from routine antenatal records to advanced biomolecular signatures—to generate individualised risk profiles. Techniques such as random forests, support vector machines, neural networks and gradient-boosting frameworks have demonstrated enhanced predictive accuracy compared with conventional statistical approaches. Deep learning architectures are beginning to exploit high-dimensional data sources, including imaging and transcriptomic profiles, to capture subtle disease signatures. Across varied healthcare settings, these models aim to improve maternal and neonatal outcomes through timely intervention, targeted monitoring and precision therapeutic strategies. The global significance of preeclampsia prediction lies in its potential to reduce maternal morbidity and perinatal mortality, particularly in resource-limited environments where early referral and intensive surveillance can be most impactful. By continually refining feature selection, assessing model calibration and validating performance across populations, machine learning approaches are evolving into robust decision-support tools for obstetric care.

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Machine Learning Applications in Preeclampsia Prediction publication trend

The graph below shows the total number of articles in machine learning applications in preeclampsia prediction across all publications each year (not limited to Nature Index journals).

Technical terms

Random forest: An ensemble learning method that constructs multiple decision trees and aggregates their predictions to improve accuracy and control overfitting.

Support vector machine (SVM): A supervised classification algorithm that identifies the optimal hyperplane separating data points of different classes with maximum margin.

Artificial neural network (ANN): A computational model inspired by biological neurons, comprising interconnected layers that transform input features through weighted connections and activation functions.

Extreme gradient boosting (XGBoost): A scalable and efficient implementation of gradient-boosted decision trees designed to optimise predictive performance through regularisation and parallelisation.

Area under the receiver operating characteristic curve (AUC): A performance metric quantifying a model’s ability to discriminate between positive and negative outcomes across all classification thresholds.

References

1. A Review on Machine Learning Deployment Patterns and Key Features in the Prediction of Preeclampsia. Machine Learning and Knowledge Extraction (2024).
2. Prediction of Preeclampsia Using Machine Learning and Deep Learning Models: A Review. Big Data and Cognitive Computing (2023).
3. Development of a prediction model on preeclampsia using machine learning-based method: a retrospective cohort study in China. Frontiers in Physiology (2022).