Polycystic Ovary Syndrome Classification Using Machine Learning Algorithm

Polycystic Ovary Syndrome (PCOS) cannot be treated completely but its symptoms can be effectively managed through medication, lifestyle modifications, or a combination of both. However, the existing approaches for detecting and predicting PCOS are inadequate. To address this issue, we propose the implementation of a machine learning system consisting of various algorithms, such as logistic regression, decision tree, random forest, and support vector machine, to facilitate early detection and prediction of PCOS. PCOS cannot be cured completely but its symptoms can be effectively managed through medication, lifestyle modifications, or a combination of both. The existing approach to the detection and prediction of PCOS is inadequate. To address this issue, we propose the implementation of a machine learning system consisting of various algorithms, such as logistic regression, decision tree, random forest, and support vector machine, to facilitate early detection and prediction of PCOS.