Optimized Feature Selection for PCOS Disease Prediction

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.