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Prediction of ectopic pregnancy using interpretable machine learning algorithms.

Arkan Aghayari1, Amir Sorayaie Azar2, Mortaza Taheri-Anganeh3

  • 1Reproductive Health Research Center, Clinical Research Institute, Urmia University of Medical Sciences, Urmia, Iran.

BMC Pregnancy and Childbirth
|October 22, 2025
PubMed
Summary

Machine learning models can predict ectopic pregnancy (EP) risk. Key factors include mid-cycle pain, genital surgery history, and dysmenorrhea, improving early detection and patient outcomes.

Keywords:
Ectopic pregnancyInterpretabilityMachine learning algorithmsPrediction

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Area of Science:

  • Reproductive Health
  • Medical Informatics
  • Machine Learning in Medicine

Background:

  • Ectopic pregnancy (EP) occurs when a blastocyst implants outside the uterus.
  • Accurate risk identification and understanding risk factor relationships are crucial for managing EP.

Purpose of the Study:

  • To develop predictive models for enhanced ectopic pregnancy risk identification.
  • To uncover relationships between known risk factors and ectopic pregnancy occurrence.

Main Methods:

  • Utilized five-fold cross-validation and Grid Search for model optimization.
  • Evaluated model performance using accuracy, AUC, and NPV.
  • Employed SHapley Additive exPlanations (SHAP) for feature significance analysis.

Main Results:

  • Random Forest (RF) achieved the highest performance (87.13% accuracy, 90.65% AUC).
  • Identified mid-cycle pain, genital surgery history, and dysmenorrhea as key predictors via logistic regression (LR) and SHAP analysis.

Conclusions:

  • Machine learning models show potential to improve clinical decision-making for ectopic pregnancy.
  • Further validation in diverse populations is needed, considering limitations like single-center data and unassessed risk factors (e.g., PIDs, IUDs).