Hybrid Machine Learning Model for Early Discovery and Prediction of Polycystic Ovary Syndrome

Female fertility is impaired by the medical disorder known as polycystic ovary syndrome (PCOS), which affects those who are of childbearing age or gradually past it. This medical condition raises the possibility of intricate long-term problems. Boosting and bagging algorithms provide exceptional recognition capabilities, particularly in the medical field. Here, presenting the hybrid machine learning model, i.e. Hybrid classifier is developed by combining the Support Vector Machine, Random Forest, and XGboosting algorithms. In order to evaluate the best machine learning technique, the hybrid model is tested using a PCOS dataset that was obtained from the repository. Basic approaches for comparing the findings of various classifiers considered including Gradient Boosting, Random Forest, Logistic Regression, AdaBoosting, SVM, Decision Tree, and MLP were taken into consideration. The hybrid model is giving 93.8% of accuracy in discovering the PCOS at early stages.