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Prediction Model of Adverse Pregnancy Outcome in Pre-Eclampsia Based on Logistic Regression and Random Forest

Lingyan Wang, Yun Mo, Peili Wang

    Alternative Therapies in Health and Medicine
    |September 29, 2023
    PubMed

    Insights

    A random forest model effectively predicts adverse pregnancy outcomes in preeclampsia (PE) patients, outperforming logistic regression. This aids clinicians in identifying high-risk individuals for timely intervention.

    Area of Science:

    • Obstetrics and Gynecology
    • Medical Informatics
    • Clinical Prediction Modeling

    Background:

    • Preeclampsia (PE) poses significant risks for adverse pregnancy outcomes.
    • Accurate identification of high-risk PE patients is crucial for effective management and intervention.
    • Existing prediction models may require enhancement for improved accuracy.

    Purpose of the Study:

    • To develop and evaluate a prediction model for adverse pregnancy outcomes in preeclampsia (PE).
    • To assist clinicians in identifying PE patients at high risk of adverse outcomes.
    • To provide guidance for timely treatment interventions.

    Main Methods:

    • A retrospective study of 319 PE patients was conducted.
    • Patients were categorized into adverse (93) and non-adverse (226) outcome groups.
    • Logistic regression and random forest models were constructed and compared using a 7:3 training/testing split.

    Main Results:

    • Key predictors for adverse outcomes included age, small gestational age, clinical symptoms, 24-hour proteinuria, platelet count (PLT), AST, and D-Dimer levels.
    • The random forest model demonstrated superior predictive performance compared to the logistic regression model in the test set.
    • Significant influencing factors for adverse pregnancy outcomes in PE patients were identified.

    Conclusions:

    • The random forest model exhibits robust stability and superior prediction efficiency for adverse pregnancy outcomes in PE.
    • This model can enhance clinical decision-making for managing high-risk preeclampsia pregnancies.
    • Further validation of the random forest model in diverse clinical settings is warranted.
    Abstract

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