Machine Learning Model for Adverse Pregnancy Outcomes in PCOS

10 Jul 20265 min read0 viewsJournal Feed

GIST (Key Takeaways)

Introduction Polycystic ovary syndrome (PCOS) is a prevalent endocrine condition in reproductive-aged women, which is associated with adverse maternal and neonatal outcomes in pregnancy. In the present study, pregnancy complications - like gestational diabetes, preterm birth, hypertension, low birth weight and neonatal intensive care unit admission - will be predicted with high accuracy by machine learning-based prediction models.
Methods and analysis There are three substudies: (1) Developing a researcher-made questionnaire by literature review, data collection from existing medical records in maternal and neonatal systems of women with PCOS who have given birth, collected from hospitals and private clinics of East Azerbaijan Province and Tehran. Approximately 800 - 1000 women with PCOS will be included in the study and then data preprocessing will be performed.
(2) Developing machine learning-based models for predicting adverse pregnancy outcomes in women with PCOS using decision trees, random forests, Extreme Gradient Boosting, support vector machines, k-nearest neighbours and neural network algorithms and (3) Developing a user-friendly application or interface that will operate on various devices.

Clinical Editorial

BMJ Open published a clinical update in Research Highlights on 10 Jul 2026.

The item focuses on Development of a machine learning-based model for predicting adverse pregnancy outcomes in women with polycystic ovary syndrome (PCOS): a retrospective observational study protocol.

Review the original article for the full source wording and details.