Diagnosis of Polycystic Ovary Syndrome using Machine Learning Algorithms

Artificial intelligence can be used to manage enormous amount of clinical data with great accuracy and precision in healthcare systems for diagnostic reasons. we used machine learning algorithms such as AdaBoost, Gradient Boosting, KNN, Random Forest, and Logistic Regression to diagnose PCOS based on patient clinical data. The data was analysed, and the algorithms' accuracy and precision were validated. The Random Forest algorithm in the diagnosis of PCOS on given data has the maximum accuracy, i.e., 96 percent, according to the validation metrics.