Abstract
PCOS is the most predominant endocrine problem among women of reproductive age, which affects ovaries and causes irregularities in menstrual cycles, weight gain, and hirsutism [Bulsara J., P. Patel, A. Soni, S. Acharya (2021) A review: Brief insight into PCOS, Endocrine and Metabolic Sci., 3, 1–7]. The condition probably results from a mix of causes, including qualities and ecological elements. The aim of this study is to investigate the performance of Binarized Butterfly Optimization Algorithm, Binarized Grey Wolf Optimization Algorithm, Binarized Genetic Algorithm and Binarized Cuckoo Optimization Algorithm in terms of classification accuracy. Kaggle PCOS dataset is used for this work and it has 541 records and 43 attributes. The study aims to investigate the performance of four optimization algorithms in terms of classification accuracy for predicting PCOS disease. The BGWO optimization algorithm outperformed the other algorithms with 99% accuracy, while the BGA produced the most optimal subset. The study highlights the significance of feature selection in improving the performance of classification algorithms.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
