Utilizing machine learning to predict the risk factors of episiotomy in parturient women
Utilizing a clinical dataset and various machine learning models to assess the risk factors of episiotomy resulted in promising results. Further research, focusing on intrapartum clinical data and perspectives of the birth attendant, is necessary to enhance the accuracy of predictions.
Authors
- Nasibeh Roozbeh (ORCID: https://orcid.org/0000-0002-8492-0087)
- Vahid Mehrnoush (ORCID: https://orcid.org/0000-0002-1047-7713)
- Mojdeh Banaei (ORCID: https://orcid.org/0000-0003-4929-7645)
- Fatemeh Darsareh (ORCID: https://orcid.org/0000-0003-2644-792X)
- Farideh Montazeri
- Mohammad Sadegh Vahidi Farashah
Institutions
- Hormozgan University of Medical Sciences (IR)
Publication Details
- Journal
- AJOG Global Reports
- Published
- 2024-11-13
- DOI
- https://doi.org/10.1016/j.xagr.2024.100420
- Citations
- 17
- Primary Topic
- Pelvic floor disorders treatments
- Type
- article
- Field-Weighted Citation Impact
- 7.99
Funders
- Hormozgan University of Medical Sciences