Search results for: PREECLAMSPIA RISK

• Preeclampsia Risk Prediction Using Machine Learning Methods Trained on Synthetic Data

  Publication

  • M. Mazur-Milecka
  • N. Kowalczyk
  • K. Jaguszewska
  • D. Zamkowska
  • D. Wójcik
  • K. Preis
  • H. Skov
  • S. R. Wagner
  • P. Sandager
  • M. Sobotka
  • J. Rumiński

  - Year 2024

  This paper describes a research study that investigates the use of machine learning algorithms on synthetic data to classify the risk of developing preeclampsia by pregnant women. Synthetic datasets were generated based on parameter distributions from three real patient studies. Four models were compared: XGBoost, Support Vector Machine (SVM), Random Forest, and Explainable Boosting Machines (EBM). The study found that the XGBoost...

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