Comparative study of machine learning approaches integrated with genetic algorithm for IVF success prediction.

Journal: PloS one
Published Date: Oct 11, 2024

Abstract

INTRODUCTION: IVF is a widely-used assisted reproductive technology with a consistent success rate of around 30%, and improving this rate is crucial due to emotional, financial, and health-related implications for infertile couples. This study aimed to develop a model for predicting IVF outcome by comparing five machine-learning techniques.

Authors

  • Shirin Dehghan
    Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Reza Rabiei
    Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Hamid Choobineh
  • Keivan Maghooli
    Biomedical Engineering Department, Science and Research Branch, Islamic Azad University, Tehran, Iran.
  • Mozhdeh Nazari
    Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Mojtaba Vahidi-Asl
    Faculty of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran.

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