Enhanced ML Framework for PCOS Diagnosis: Optimised XGBoost Model
Polycystic ovary syndrome (PCOS) is a very common endocrine disorder that affects women of child bearing age across the globe with a global prevalence of between 8-13 percent. Although it is a common disease, PCOS is highly undiagnosed, as the clinical picture is heterogeneous, and the diagnosis is based on costly hormonal tests and ultrasound studies. Later diagnosis causes chronic complications such as infertility, diabetes mellitus type 2, heart diseases, and endometrial cancer. This article reports a machine learning model of early and accurate prediction of PCOS based on an ensemble of Random Forest and XGBoost classifiers that will be trained on a multi-feature clinical dataset of hormonal measurements, anthropometric measurements, ultrasonographic results, and lifestyle factors. The dataset, which is obtained on the publicly accessible Kaggle PCOS dataset (n=539 patients, n=41 features), was preprocessed with a severe approach such as missing value imputation, label encoding, feature selection through Recursive Feature Elimination (RFE), and Synthetic Minority Oversampling Technique (SMOTE) to handle the imbalance in the classes. Experimental performance shows that XGBoost has the best performance with an accuracy of 92.8, F1-score of 0.922, and AUC-ROC of 0.971 whilst the random forest has an accuracy of 91.3 and AUC-ROC of 0.963 and is way better than the baseline classifiers such as logistic regression (81.2) and The most discriminative predictors based on the analysis of feature importance are follicle count, anti-Mullerian hormone (AMH) level, FSH:LH ratio, and cycle irregularity. The suggested system presents an efficient, non-invasive, and scaled clinical decision support tool in gynaecology practice.