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Prediction of Preeclampsia Using Machine Learning: A Systematic Review.

Vinayak Malik¹, Neha Agrawal², Sonal Prasad²

¹Computer Science, University of Wisconsin, Madison, USA.

|January 21, 2025

Summary

This summary is machine-generated.

Machine learning models show promise for predicting preeclampsia, a major cause of maternal mortality. Balancing predictive accuracy with interpretability is key for clinical use.

Keywords:

artificial intelligence deep learning machine learning preeclampsia risk of bias systematic review

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Area of Science:

Obstetrics and Gynecology
Medical Informatics
Artificial Intelligence in Healthcare

Background:

Preeclampsia significantly contributes to maternal and perinatal morbidity and mortality worldwide.
Early prediction of preeclampsia is crucial for timely intervention, such as aspirin prophylaxis.
Machine learning (ML) is emerging as a powerful tool for disease prediction and prognosis.

Purpose of the Study:

To review methodologies, predictors, and performance of ML models for preeclampsia prediction.
To emphasize comparative advantages, challenges, and clinical applicability of ML in preeclampsia.
To explore the potential of artificial intelligence and deep learning in managing preeclampsia.

Main Methods:

Systematic literature search of PubMed, Cochrane, and Scopus (last 10 years).
Keywords included "preeclampsia", "risk factors", "machine learning", "artificial intelligence", and "deep learning".
11 eligible studies were included after screening 325 records, assessing risk of bias.

Main Results:

A wide range of predictors were used, including clinicodemographic data, lab results, Doppler ultrasound, genotypic data, and fundal images.
Over ten different ML models were employed across studies from various countries.
Models like XGBoost, random forest, and neural networks showed high predictive accuracy (AUC 0.76-0.97).

Conclusions:

ML models demonstrate significant potential for accurate preeclampsia prediction.
Interpretability of 'black box' models is a critical ethical consideration for clinical adoption.
Future research should focus on diverse population validation and balancing performance with interpretability for improved maternal outcomes.