Early Detection Of Polycystic Ovarian Disease Using Optimized Machine Learning Technique

Polycystic ovary syndrome (PCOS) and polycystic ovary disorder (PCOD) are widespread endocrine conditions affecting millions of women around the world. These disorders can result in considerable reproductive challenges and metabolic complications. By providing the urgent need for effective and accessible diagnostic methodologies, the proposed study aims to enhance the efficiency and interpretability of Artificial Neural Network (ANN) techniques for the early detection of these conditions through innovative data pre-processing strategies and progressive training data modules. This research addresses a critical gap in the literature by systematically identifying and eliminating noisy data during the pre-processing phase. The Tarsier Optimization Algorithm (TOA), inspired by the adaptive behaviours of Tarsiers is introduced to facilitate efficient exploration of the solution space. This approach refines the dataset used in model training while emphasizing the importance of feature significance. By leveraging TOA's adaptive strategies, the clarity and performances of the progressive ANN (PANN-TOA) are improved, ensuring robust handling of noisy data. The methodology highlights the necessity of optimizing data processing to deepen the understanding of the complex manifestations and health outcomes associated with PCOS and PCOD. Ultimately, the new work aims to provide a robust framework that enhances diagnostic accuracy of about 95% and informs personalized treatment strategies, paving the way for improved reproductive health outcomes and empowering individuals to make informed health decisions. Thus, the results show that the PANN performed better than previous machine learning (ML) models, demonstrating its capacity to identify intricate patterns in clinical data.