Previously submitted to: JMIR Medical Informatics (no longer under consideration since May 14, 2026)
Date Submitted: Jul 17, 2025
Open Peer Review Period: Aug 1, 2025 - Sep 26, 2025
(closed for review but you can still tweet)
NOTE: This is an unreviewed Preprint
Warning: This is a unreviewed preprint (What is a preprint?). Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn (a note "no longer under consideration" will appear above).
Peer review me: Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period (in this case, a "Peer Review Me" button to sign up as reviewer is displayed above). All preprints currently open for review are listed here. Outside of the formal open peer-review period we encourage you to tweet about the preprint.
Citation: Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author).
Final version: If our system detects a final peer-reviewed "version of record" (VoR) published in any journal, a link to that VoR will appear below. Readers are then encourage to cite the VoR instead of this preprint.
Settings: If you are the author, you can login and change the preprint display settings, but the preprint URL/DOI is supposed to be stable and citable, so it should not be removed once posted.
Submit: To post your own preprint, simply submit to any JMIR journal, and choose the appropriate settings to expose your submitted version as preprint.
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Machine Learning-Based Predictive Models for Identifying Fetal Growth Restriction in Patients With Early-Onset Preeclampsia: Retrospective Study
ABSTRACT
Background:
Background:
Fetal growth restriction (FGR) is a common and severe complication of early-onset preeclampsia (PE, ≤34 weeks), significantly increasing risks of perinatal mortality and morbidity. Current prediction methods lack both accuracy and clinical interpretability, which may delay interventions.
Objective:
Objective:
This study aimed to develop and validate machine learning (ML) models to predict FGR in patients with early-onset PE using routinely available clinical parameters.
Methods:
Methods:
We conducted a retrospective study of 711 patients with early-onset PE (n=238 with FGR, n=473 without FGR) from Fujian Maternity and Child Health Hospital (2014-2024). After rigorous variable selection using univariate analysis and LASSO regression, 8 ML algorithms including Logistic Regression (LR), Naive Bayes (NB), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Support Vector Machine (SVM), Gradient Boosting Decision Tree (GBDT), Multilayer Perceptron (MLP) and Elastic Network (EN) were trained on 70% of the data and validated on 30% of the data. Model performance was evaluated using sensitivity, specificity, accuracy, precision, F1-Score, Area Under the Receiver Operating Characteristic (AUROC), Area Under the Precision-Recall Curve (AUPRC) and calibration curves. Meanwhile, multivariate logistic regression was used to evaluate the independent predictive variables of each variable in the prediction model. The Shapley Additive Explanations (SHAP) method provided model interpretability.
Results:
Results:
The MLP model demonstrated superior performance with AUROCs of 0.872 (training, n=158 with FGR, 31.8%) and 0.874 (validation, n=214 with FGR, 37.4%) among 8 ML models. Key predictive variables included pre-pregnancy body mass index (BMI), fundal height (FH), anemia, hyperuricemia, urinary microprotein (MAU) and fetal ultrasound biometric ratios (head circumference abdominal circumference ratio (HC/AC), umbilical artery systolic-to-diastolic ratio (UA S/D), umbilical artery blood flow pulsation index (UA PI)). Furthermore, HC/AC, BMI, UA S/D and hyperuricemia were found to be the most influential predictors in ML via SHAP. Consistent with SHAP results, similar to the results of SHAP, this study found that BMI (protective factor, OR=0.905, P=.003), HC/AC (risk factor, OR=2.372, P<.001), anemia (risk factor, OR=1.914, P=.006) and hyperuricemia (risk factor, OR=1.631, P=.028) were independent risk factors for FGR in patients with early-onset PE by multivariate logistic regression analysis.
Conclusions:
Conclusions:
Our MLP-based model accurately predicts FGR in early-onset PE patients using clinically accessible parameters. The integration of ultrasound biometric ratios and maternal biomarkers provides a practical tool for early risk stratification, with SHAP enhancing clinical interpretability for real-world application.
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.