Department of Artificial Intelligence and Machine Learning, Bangalore Institute of Technology, Visvesveraya Technological University, Belagavi, Karnataka, India.
Infertility, metabolic issues, and hormone imbalance are common symptoms of PCOS, a common endocrine illness affecting women of reproductive age. A various machine learning technique are used in the research for PCOS severity grading and prediction. Recursive Feature Elimination (RFE) was used to choose features first, and then Random Forest and Logistic Regression, two supervised classifiers, applied. The models' efficacy in predicting PCOS was validated by their strong accuracy and AUC ratings. Anti-Müllerian Hormone (AMH) was used as a crucial grading marker after patients were categorized into severity categories (Severe, Moderate, and Low) based on clinical criteria using unsupervised clustering methods, specifically K-Means and Agglomerative Clustering. Well-separated clusters were shown by the silhouette scores used to evaluate the clustering models. A comprehensive framework for early PCOS detection and phenotypic grading is provided by the combination of supervised and unsupervised techniques, which also offers insightful information for individualized treatment plans.
V. C. Gandhi and S. Majumdar, "A comparative study for detecting polycystic ovary syndrome using a machine learning framework," in Proc. IEEE Int. Conf. Comput. Methodol. Commun. (ICCMC), Dec. 2023.
R. Nidhi, V. Padmalatha, R. Nagarathna, and R. Amritanshu, “Prevalence of Polycystic Ovarian Syndrome in Indian Adolescents,” Journal of Pediatric and Adolescent Gynecology, vol. 24, no. 4, pp. 223–227, Aug. 2011, doi: 10.1016/j.jpag.2011.03.002.
R. Azziz et al., “The Androgen Excess and PCOS Society criteria for the polycystic ovary syndrome: the complete task force report,” Fertility and Sterility, vol. 91, no. 2, pp. 456–488, Feb. 2009, doi: 10.1016/j.fertnstert.2008.06.035.
R. C. Deo, “Machine Learning in Medicine,” Circulation, vol. 132, no. 20, pp. 1920–1930, Nov. 2015, doi: 10.1161/circulationaha.115.001593.
Y. Kumar et al., "PCOS diagnosis using machine learning techniques: A review," Biomed. Signal Process. Control, vol. 68, 102648, May 2021.
A. Rawat and V. Varma, "A machine learning model for severity classification in PCOS using clustering and correlation techniques," Int. J. Healthc. Inf. Syst. Informatics, vol. 15, no. 4, pp. 1–15, Oct.–Dec. 2020.
H. K. Hosain, M. K. Mehedi, and I. E. Kabir, "PCONet: A convolutional neural network architecture to detect polycystic ovary syndrome (PCOS) from ovarian ultrasound images," in Proc. 8th Int. Conf. Eng. Emerg. Technol. (ICEET), Oct. 2022, pp. 296–302.
A. Divekar and A. Sonawane, "Leveraging AI for automatic classification of PCOS using ultrasound imaging," arXiv preprint arXiv:2501.01984, Dec. 2024.
M. Uddin et al., "Intelligent detection for polycystic ovary syndrome (PCOS)," in Proc. IEEE Int. Conf. Bioinform. Biomed. (BIBM), 2022.
H. Elmannai., “Polycystic Ovary Syndrome Detection Machine Learning Model Based on Optimized Feature Selection and Explainable Artificial Intelligence,” Diagnostics, vol. 13, no. 8, p. 1506, Apr. 2023, doi: 10.3390/diagnostics13081506.
A. Alshakrani., "Optimized machine learning for early detection of PCOS: an ensemble-based approach with feature selection," Sensors, vol. 25, no. 4, 2025.
S. Faris and S. Miften, "Automated high precision PCOS detection through a segment-based machine learning pipeline," Int. J. Med. Inform., 2023.
D. Hdaib., "Comparative performance of ML classifiers for early PCOS detection," IEEE Access, 2022.
A. M. Gupta, S. S. Shetty, R. M. Joshi, and R. M. Laban, “Succinct Differentiation of Disparate Boosting Ensemble Learning Methods for Prognostication of Polycystic Ovary Syndrome Diagnosis,” 2021 International Conference on Advances in Computing, Communication, and Control (ICAC3), pp. 1–5, Dec. 2021, doi: 10.1109/icac353642.2021.9697163.
N. Khanna., "Explainable AI based pipeline using SHAP and LIME for PCOS detection," in Proc. IEEE Region 10 Conf. (TENCON), 2023.
S. Venkatalakshmi and P. Regina, "A comprehensive overview of ML and DL approaches for PCOS diagnosis," IEEE J. Biomed. Health Inform., vol. –, 2024.
A. Modi., "International evidence-based guideline for the assessment and management of polycystic ovary syndrome," Semin. Reprod. Med., vol. 41, no. 1–02, pp. 3–4, Mar. 2023.
Follicles Net, "Follicles Net: An efficient tool for the diagnosis of polycystic ovarian syndrome," Diagnostics, vol. –, 2024.
"Systematic review: Detection of polycystic ovary syndrome using AI/ML," Front. Endocrinol., 2023.
A. Bharati , "Federated learning for PCOS treatment recommendations," arXiv preprint, 2023.
R. Dewailly, J. Laven, D. H. C. De Groot, S. E. M. Dolleman, B. C. J. M. Fauser, and E. S. Teede, "Anti-Müllerian hormone as a diagnostic and prognostic marker in polycystic ovary syndrome," Hum. Reprod. Update, vol. 22, no. 5, pp. 726–740, Sep. 2016, doi: 10.1093/humupd/dmw020.
The authors confirm contribution to the paper as follows:
Conceptualization: Suvika K V and Jyothi D G;
Methodology: Suvika K V;
Software: Jyothi D G;
Data Curation: Suvika K V and Jyothi D G;
Writing- Original Draft Preparation: Suvika K V;
Visualization: Jyothi D G;
Investigation: Suvika K V and Jyothi D G;
Supervision: Jyothi D G;
Validation: Suvika K V;
Writing- Reviewing and Editing: Suvika K V and Jyothi D G;
All authors reviewed the results and approved the final version of the manuscript.
Acknowledgements
Author(s) thanks to Dr.Jyothi D G for this research completion and support.
Funding
No funding was received to assist with the preparation of this manuscript.
Ethics declarations
Conflict of interest
The authors have no conflicts of interest to declare that are relevant to the content of this article.
Availability of data and materials
Data sharing is not applicable to this article as no new data were created or analysed in this study.
Author information
Contributions
All authors have equal contribution in the paper and all authors have read and agreed to the published version of the manuscript.
Corresponding author
Suvika K V
Department of Computer Science and Engineering, Bangalore Institute of Technology, Visvesveraya Technological University, Belagavi, Karnataka, India.
Open Access This article is licensed under a Creative Commons Attribution NoDerivs is a more restrictive license. It allows you to redistribute the material commercially or non-commercially but the user cannot make any changes whatsoever to the original, i.e. no derivatives of the original work. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/
Cite this article
Suvika K V and Jyothi D G, “A Machine Learning Approach for Efficient Identification and Severity Grading of PCOS and PCOD Using Optimized Features”, Journal of Machine and Computing, vol.5, no.4, pp. 1994-2005, October 2025, doi: 10.53759/7669/jmc202505156.
