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Updated: May 24, 2025

In utero Measurement of Heart Rate in Mouse by Noninvasive M-mode Echocardiography
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Proposing a machine learning-based model for predicting nonreassuring fetal heart.

Nasibeh Roozbeh1, Farideh Montazeri1, Mohammadsadegh Vahidi Farashah1

  • 1Mother and Child Welfare Research Center, Hormozgan University of Medical Sciences, Bandar Abbas, Iran.

Scientific Reports
|March 6, 2025
PubMed
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Machine learning models can predict nonreassuring fetal heart (NFH) conditions. Random forest classification demonstrated the highest performance, offering potential for improved perinatal care.

Area of Science:

  • Obstetrics and Gynecology
  • Medical Informatics
  • Machine Learning in Healthcare

Background:

  • Predicting nonreassuring fetal heart (NFH) patterns is critical for reducing perinatal complications.
  • Limited research exists on identifying key predictors for NFH.

Purpose of the Study:

  • To evaluate the efficacy of machine learning (ML) models in predicting NFH.
  • To identify demographic, obstetric, maternal, and neonatal factors associated with NFH.

Main Methods:

  • Retrospective analysis of singleton births (≥28 weeks gestation) from the Iranian Maternal and Neonatal Network (January 2020 - January 2022).
  • Four ML models (Decision Tree, Random Forest, Extreme Gradient Boost, k-NN) were developed and compared.
  • Chi-Square test identified potential NFH predictors (p < 0.05).
Keywords:
Fetal heartMachine learningNonreassuring fetal heartNonreassuring fetal statusX gradient boost model

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  • Model performance was assessed using AUROC, accuracy, precision, recall, and F1 score.

    • Main Results:

    • The incidence of NFH was 9.2%.
    • NFH was associated with intrauterine growth restriction, late/post-term/preterm births, preeclampsia, placental abruption, primiparity, induced labor, and male fetus; doula support was associated with lower incidence.
    • Random Forest (AUROC: 0.77) and k-NN (AUROC: 0.77) showed the best performance, with Random Forest achieving 0.77 accuracy and 0.72 precision.

    Conclusions:

    • Machine learning models, particularly Random Forest, show promising performance in predicting NFH.
    • Further research is warranted to solidify the role of ML in predicting NFH and improving perinatal outcomes.