International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

PCOS Detection Using Machine Learning

G Dinesh ReddyMCA student, Department of Computer Applications, Mohan Babu University, Tirupati, Andhra Pradesh, IndiaDr. M. Sireesh KumarAssociate Professor, Department of Computer Applications, Mohan Babu University, Tirupati, Andhra Pradesh, India

Vol 9 No 25 (2025): Volume 9, Special Issue of INSPIRE’25 April 2025 | Pages: 364-370

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 24-04-2025

doi Logo doi.org/10.47001/IRJIET/2025.INSPIRE59

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Abstract

Reproductive-age women worldwide suffer from metabolic problems, hormonal imbalance, and irregular menstruation due to PCOS. Complex symptoms of PCOS often lead to misdiagnosis or underdiagnosis, causing suffering and increasing the risk of obesity, diabetes, and cardiovascular disease. Treating these symptoms requires early, correct diagnosis. The project tests machine learning techniques such as Linear Regression, Ridge Regression, Lasso Regression, Random Forest Regressor, K-Nearest Neighbors, Decision Tree Regressor, and Support Vector Machines employing Mean Squared Error to address diagnostic issues. Since it handled noisy and non-linear data better, the Random Forest Regressor was best. Django-based internet applications and predictive algorithm help clinicians identify PCOS risk. The approach instantly assesses risk using BMI, age, blood pressure, and lifestyle factors. This simple method lets clinicians identify high-risk patients for rapid intervention and personalized treatment. Accuracy, scalability, and usability tests validated the system's clinical value. Finally, our machine learning-based solution will improve early PCOS identification, clinical resource use, and global women's health.

Keywords

PCOS, Machine Learning, Random Forest Regressor, Linear Regression, Ridge Regression, Lasso Regression, Decision Tree, K-Nearest Neighbors, Support Vector Machine, Django, Prediction, Endocrine Disorder, Women’s Health, Mean Squared Error, Real-time Application


Citation of this Article

G Dinesh Reddy, & Dr. M. Sireesh Kumar. (2025). PCOS Detection Using Machine Learning. In proceeding of International Conference on Sustainable Practices and Innovations in Research and Engineering (INSPIRE'25), published by IRJIET, Volume 9, Special Issue of INSPIRE’25, pp 364-370. Article DOI https://doi.org/10.47001/IRJIET/2025.INSPIRE59

Licence Copyright (c) 2026 International Research Journal of Innovations in Engineering and Technology creative common licence This work is licensed under Creative common Attribution Non Commercial 4.0 Internation Licence
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