Comparative study of machine learning approaches integrated with genetic algorithm for IVF success prediction
These findings underscore the potential of machine learning and feature selection techniques to assist IVF clinicians in providing more accurate predictions, enabling tailored treatment plans for each patient. Future research and validation can further enhance the practicality and reliability of these predictive models in clinical IVF practice.
Authors
- Mozhdeh Nazari
- Reza Rabiei (ORCID: https://orcid.org/0000-0003-0771-7306)
- Keivan Maghooli (ORCID: https://orcid.org/0000-0003-0980-0154)
- Shirin Dehghan (ORCID: https://orcid.org/0000-0002-9616-9251)
- Hamid Choobineh
- Mojtaba Vahidi-Asl (ORCID: https://orcid.org/0000-0003-4964-992X)
Institutions
- Islamic Azad University, Science and Research Branch (IR)
- Shahid Beheshti University (IR)
- Shahid Beheshti University of Medical Sciences (IR)
- Tehran University of Medical Sciences (IR)
Publication Details
- Journal
- PLoS ONE
- Published
- 2024-10-11
- DOI
- https://doi.org/10.1371/journal.pone.0310829
- Citations
- 16
- Primary Topic
- Ovarian function and disorders
- Type
- article
- Field-Weighted Citation Impact
- 8.59