Prediction of ectopic pregnancy using interpretable machine learning algorithms
This investigation illustrates the potential for machine learning models to improve clinical decision-making and optimize patient outcomes in cases of ectopic pregnancy. Further validation in diverse populations is required due to the constraints of single-center data and the absence of critical risk factors, such as the use of PIDs and IUDs.
Authors
- Mortaza Taheri‐Anganeh (ORCID: https://orcid.org/0000-0002-9659-7491)
- Sonia Sadeghpour (ORCID: https://orcid.org/0000-0002-6368-0598)
- Yousef Mohammadpour (ORCID: https://orcid.org/0000-0001-5078-733X)
- Amir Sorayaie Azar (ORCID: https://orcid.org/0000-0001-7797-7790)
- Hojat Ghasemnejad‐Berenji
- Jamshid Bagherzadeh (ORCID: https://orcid.org/0000-0003-2497-0186)
- Arkan Aghayari
Institutions
- Urmia University (IR)
- Urmia University of Medical Sciences (IR)
Publication Details
- Journal
- BMC Pregnancy and Childbirth
- Published
- 2025-10-21
- DOI
- https://doi.org/10.1186/s12884-025-08179-7
- Citations
- 1
- Primary Topic
- Ectopic Pregnancy Diagnosis and Management
- Type
- article
- Field-Weighted Citation Impact
- 1.88
Funders
- Urmia University
- Urmia University of Medical Sciences