Enhanced Diagnosis of Polycystic Ovarian Syndrome Using Stacking Classifier with Random Forest As a Meta Learner
Year: 2025
Type: article
Abstract: Polycystic Ovary Syndrome (PCOS) is considered a very serious health problem among the women of childbirth age. Early and accurate detection is essential for effective management but remains challenging due to symptom variability and subjective traditional diagnostics. Machine learning (ML) algorithms have recently emerged as promising tools for PCOS diagnosis by analyzing multidimensional clinical, hormonal, and imaging data. This study evaluates and compares multiple supervised ML methods—including Gradient Boosting (XGBoost, LightGBM, and CatBoost), AdaBoost, Logistic Regression, Naive Bayes, Random Forest, Decision Tree, Support Vector Machine (SVM), and Stacking classifiers—on a publicly available dataset of 541 patients. Various evaluation measures like Receiver Operating Characteristic curve (AUC-ROC), recall, accuracy, F1-score, and precision are used for the evaluation. Stacking classifier outperforms all single models used in this research. The Stacking classifier with Random Forest as meta-learner achieved 98% accuracy with AUC of 98%. These findings demonstrate that advanced ensemble ML models can robustly detect PCOS, with potential for integration into clinical workflows for early, objective diagnosis. Future work should focus on multi-center validation and explainable AI to enhance clinical trust and deployment.
DOI: 10.14419/36w9s378
Authors: Pragati Patil, Nandini Chaudhari
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4417036433
FWCI:
Cites:
Cited by:
Related to: