Advancing Polycystic Ovary Syndrome Diagnosis: A Holistic Ensemble-Based Machine Learning Approach

Polycystic ovary syndrome (PCOS) is a complex hormonal disorder that presents a diagnostic challenge due to its varied manifestations. Early detection of PCOS is crucial for timely intervention and management. In this paper, we propose a machine-learning-based approach for the early diagnosis of PCOS, leveraging demographic data to train a model that achieves high accuracy. Our study highlights the significance of data scaling and normalization in improving the model's performance. The results demonstrate that ensemble methods, specifically Random Forest and Extra Trees Classifier, outper-form traditional methods, exhibiting an accuracy of 94 % for PCOS detection. Feature selection analysis reveals that clinical and demographic features, including age, body mass index (BMI), and hormonal levels, play a pivotal role in PCOS detection, with the combination of features yielding superior performance. Model optimization through parameter fine-tuning and experimentation with different ensemble methods enhance the models' predictive capability. Our proposed methodology addresses the limitations of existing algorithms, ensuring robust and accurate PCOS diagnosis. The implementation of our approach demonstrates efficiency in achieving high accuracy and performance for PCOS detection. Therefore, the future for early diagnosis of PCOS with this innovative and compatible machine-learning approach is promising, offering the potential to revolutionize PCOS diagnosis and aid in timely intervention and personalized management strategies. Thus, machine learning is a valuable adjunct to clinical examination, hormonal assays, and ultrasound, as it holds great promise as a powerful tool for early detection and diagnosis of PCOS, enhancing healthcare outcomes for women with this disorder.