Machine Learning Algorithms Versus Classical Regression Models in Pre-Eclampsia Prediction: A Systematic Review.

Journal: Current hypertension reports
PMID: 38806766

Abstract

PURPOSE OF REVIEW: Machine learning (ML) approaches are an emerging alternative for healthcare risk prediction. We aimed to synthesise the literature on ML and classical regression studies exploring potential prognostic factors and to compare prediction performance for pre-eclampsia.

Authors

  • Sofonyas Abebaw Tiruneh
    Monash Centre for Health Research and Implementation, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia.
  • Tra Thuan Thanh Vu
    Monash Centre for Health Research and Implementation, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia.
  • Daniel Lorber Rolnik
    Department of Obstetrics and Gynecology, Monash University, Melbourne, VIC, Australia.
  • Helena J Teede
    Monash Centre for Health Research and Implementation, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia.
  • Joanne Enticott
    Monash Centre for Health Research and Implementation, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia. joanne.enticott@monash.edu.

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