Prediction of chromosomal abnormalities in the screening of the first trimester of pregnancy using machine learning methods: a study protocol.

Journal: Reproductive health
PMID: 38961456

Abstract

BACKGROUND: For women in the first trimester, amniocentesis or chorionic villus sampling is recommended for screening. Machine learning has shown increased accuracy over time and finds numerous applications in enhancing decision-making, patient care, and service quality in nursing and midwifery. This study aims to develop an optimal learning model utilizing machine learning techniques, particularly neural networks, to predict chromosomal abnormalities and evaluate their predictive efficacy.

Authors

  • Mahla Shaban
    Department of Midwifery, Research Student Committee, Mashhad University of Medical Sciences, Mashhad, Iran.
  • Sanaz Mollazadeh
    Department of Midwifery, Research Student Committee, Mashhad University of Medical Sciences, Mashhad, Iran.
  • Saeid Eslami
    Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
  • Fatemeh Tara
    Department of Obstetrics and Gynecology, Faculty of Medicine, Mashhad University of Medical, Mashhad, Iran.
  • Samaneh Sharif
    Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
  • Fatemeh Erfanian Arghavanian
    Nursing and Midwifery Care Research Center, Mashhad University of Medical Sciences, Mashhad, Iran. erfanianf@mums.ac.ir.

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