Sine cosine algorithm-based feature selection for improved machine learning models in polycystic ovary syndrome diagnosis

Polycystic Ovary Syndrome (PCOS) is a hormonal state that affects women within the reproductive age group. Early and accurate diagnosis of medical conditions is crucial for the successful implementation of medication and prevention of significant health problems. In recent times, there has been an increasing amount of evidence suggesting that machine learning (ML) methods have significant potential in facilitating the diagnosis and classification of PCOS. The objective of this research is to evaluate the viability of using several machine learning algorithms, such as Random Forests, Decision Trees, Artificial Neural Networks, and XGBoost, for the purpose of classifying PCOS using clinical and laboratory features. Furthermore, the use of the Sine Cosine Algorithm was implemented for the purpose of selecting features in order to improve the accuracy and efficiency of the model. A sample of 500 individuals was selected for the purpose of training and assessing the models, with half of the participants (250) diagnosed with PCOS and the other half not exhibiting the disease. The findings suggest that the classification algorithms had strong performance in classifying PCOS, with XGBoost having the best level of performance. In addition, the use of the Sine Cosine Algorithm resulted in enhanced model precision and effectiveness via the identification of the most pertinent features for the classification objective. The results of this study emphasise the significant capabilities of machine learning methods, namely XGBoost and the Sine Cosine Algorithm, in aiding the diagnosis and classification of PCOS in a medical setting. Additional investigation is required in order to substantiate the models and prove their clinical significance in the provision of patient care.