Predicting PCOS through Artificial Intelligence Modeling with Biomarkers: A Preliminary Study for Prescribing Exercise in Iranian Women

Document Type : Original Article

Authors

1 PhD Student in Exercise Physiology, School of Sports Sciences, University of Mazandaran, Babolsar, Iran.

2 Professor, Department of Exercise Physiology, School of Sports Sciences, University of Mazandaran, Babolsar, Iran. Professor, Department of Exercise Physiology, Athletic Performance and Health Research Centre, School of Sports Sciences, University of Mazandaran, Babolsar, Iran.

3 Assistant Professor, Department of Obstetrics and Gynecology, Faculty of Medicine, Islamic Azad University of Tehran, Tehran, Iran.

Abstract

Introduction: Childbearing and rejuvenating society are serious challenges in today's world. Polycystic ovary syndrome (PCOS), which is associated with obesity, has become a common concern for fertility. Early diagnosis of PCOS can improve the treatment process and the quality of life of the family. The present study was conducted with aim to train machine learning models with hormonal marker data and metabolic syndrome to identify PCOS and then present exercise strategies for infertile women.
Methods: Data from 1000 infertile women aged 20-45 years with and without PCOS were initially processed, and subsequently, 500 individuals were selected using various methods, including random forests (RF), recurrent feature elimination (RFE), and interaction (MI), to identify important features and incorporate them into different algorithms.
Results: Training Decision Tree (DT) model with feature selection by the RF method had the highest accuracy in diagnosis compared to others. Also, according to the results of feature selection by RFE, MI, and RF methods, it was shown that, in addition to sex hormones, fasting glucose, cholesterol, high-density lipoprotein, vitamin D3, and thyroid-related hormones are prominent factors that play a role in predicting PCOS with high accuracy.
Conclusion: Diagnosis of PCOS and investigation of its related biomarkers with artificial intelligence showed that metabolic indicators, along with sex hormones, are of particular importance in the diagnosis and treatment of this disease.

Keywords

Main Subjects

References

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The Iranian Journal of Obstetrics, Gynecology and Infertility
Volume 28, Issue 11
January 2026
Pages 31-45

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