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Research Article
Online Published: 26 Feb 2026
 


Experimenting Polycystic Ovary Syndrome Detection Based on Optimized Classification Algorithms

Ayobami Ekundayo, John Kolo Alhassan, Enesi Femi Aminu, Solomon Adelowo Adepoju, Oluwaseun Adeniyi Ojerinde.


Abstract
Research on utilizing machine learning techniques models to detect for identifying Polycystic Ovary Syndrome (PCOS) has significantly increased recently. This growing focus is justified, as the disorder predominantly affects women of reproductive age and is a major contributor to infertility. Consequently, researchers employ Machine Learning techniques to address this disease. The dataset used in this study was obtained from kaggle repository. The PCOS dataset consists of 541 samples and 42 features in CSV format, comprising 364 non-PCOS cases and 177 PCOS cases. However, issues of accuracy and optimal results are still major challenge owing to the complexity of the medical data. Therefore, this study proposes to detect PCOS using Random Forest (RF) , GradientBoost (GB), and K-Nearest Neighbour (KNN) with the aid of optimized techniques such as Red Deer Algorithm (RDA) and Particle Swamp Optimization Algorithm (PSO). The RDA + RF achieved an accuracy of 82% ,RDA + KNN achieved an accuracy of 83%, RDA + GB achieved an accuracy of 81%. Also, the PSO + RF achieved an accuracy of 88%. The PSO + KNN achieved an accuracy of 92%. The three classical Machine learning models (RF, GB, and KNN) achieved accuracies of 85%, 86%, and 81% respectively. Therefore, judging from the results, the particle swam optimization approach is promising as it has shown significant improvement in terms of accuracy to detect PCOS. The model can be implemented for ground truth results application in health care management.

Key words: Polycystic Ovary Syndrome (PCOS), Random Forest (RF), GradientBoost (GB) , Red Deer Algorithm (RDA), K-Nearest Neighbour (KNN).


 
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How to Cite this Article
Pubmed Style

Ekundayo A, Alhassan JK, Aminu EF, Adepoju SA, Ojerinde OA. Experimenting Polycystic Ovary Syndrome Detection Based on Optimized Classification Algorithms. Equijost. Online First: 26 Feb, 2026.

Web Style

Ekundayo A, Alhassan JK, Aminu EF, Adepoju SA, Ojerinde OA. Experimenting Polycystic Ovary Syndrome Detection Based on Optimized Classification Algorithms. https://www.equijost.com/?mno=282642 [Access: June 27, 2026].

AMA (American Medical Association) Style

Ekundayo A, Alhassan JK, Aminu EF, Adepoju SA, Ojerinde OA. Experimenting Polycystic Ovary Syndrome Detection Based on Optimized Classification Algorithms. Equijost. Online First: 26 Feb, 2026.


Vancouver/ICMJE Style

Ekundayo A, Alhassan JK, Aminu EF, Adepoju SA, Ojerinde OA. Experimenting Polycystic Ovary Syndrome Detection Based on Optimized Classification Algorithms. Equijost, [cited June 27, 2026]; Online First: 26 Feb, 2026.


Harvard Style

Ekundayo, A., Alhassan, . J. K., Aminu, . E. F., Adepoju, . S. A. & Ojerinde, . O. A. (0) Experimenting Polycystic Ovary Syndrome Detection Based on Optimized Classification Algorithms. Equijost, Online First: 26 Feb, 2026.


Turabian Style

Ekundayo, Ayobami, John Kolo Alhassan, Enesi Femi Aminu, Solomon Adelowo Adepoju, and Oluwaseun Adeniyi Ojerinde. 0. Experimenting Polycystic Ovary Syndrome Detection Based on Optimized Classification Algorithms. Equity Journal of Science and Technology, Online First: 26 Feb, 2026.


Chicago Style

Ekundayo, Ayobami, John Kolo Alhassan, Enesi Femi Aminu, Solomon Adelowo Adepoju, and Oluwaseun Adeniyi Ojerinde. "Experimenting Polycystic Ovary Syndrome Detection Based on Optimized Classification Algorithms." Equity Journal of Science and Technology Online First: 26 Feb, 2026.


MLA (The Modern Language Association) Style

Ekundayo, Ayobami, John Kolo Alhassan, Enesi Femi Aminu, Solomon Adelowo Adepoju, and Oluwaseun Adeniyi Ojerinde. "Experimenting Polycystic Ovary Syndrome Detection Based on Optimized Classification Algorithms." Equity Journal of Science and Technology Online First: 26 Feb, 2026. Web. 27 Jun 2026


APA (American Psychological Association) Style

Ekundayo, A., Alhassan, . J. K., Aminu, . E. F., Adepoju, . S. A. & Ojerinde, . O. A. (0) Experimenting Polycystic Ovary Syndrome Detection Based on Optimized Classification Algorithms. Equity Journal of Science and Technology, Online First: 26 Feb, 2026.




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