Abstract:
In recent years, there has been a noticeable rise in the prevalence of Polycystic Ovary
Syndrome (PCOS), a complex endocrine disorder that affects a significant portion of the
population, particularly women of reproductive age. PCOS is characterized by hormonal
imbalances, irregular menstrual cycles, and the presence of multiple small cysts on the
ovaries. Beyond its reproductive implications, PCOS is associated with various metabolic
disturbances, including insulin resistance, obesity, dyslipidemia, and increased risk for type
2 diabetes and cardiovascular disease. To gain a comprehensive understanding of the
disease's severity and its multifaceted impact on women's health, distinguishing between
standard and affected diagnostic reports is imperative. In this study, we propose the
application of algorithmic models to enable early detection and raise awareness of potential
health risks associated with PCOS. Our approach is straightforward and well-suited for the
prediction of uncomplicated cases of PCOS in real-world scenarios. Our dataset, sourced
from various medical databases and clinical records, served as the foundation for our
research. We employed a wide array of classifiers, including ANN, RNN, CNN, LSTM,
BLSTM, RF, LR, GB, KNN, ABC, DT, SVM, QDA, RC, PA, GNB, and ensemble
techniques, to comprehensively explore and evaluate the predictive capabilities of each
model in identifying PCOS and its associated complications. The results yielded notable
success, with the Soft Voting classifier emerging as the most accurate, an impressive
accuracy rate of 96.58%. Our optimization efforts, which included hyperparameter tuning,
further enhanced the performance of each classifier. Based on extensive experimentation
and a review of contemporary research, our findings unequivocally endorse the Support
Vector Classifier (SVC) classifier as exceptionally proficient, demonstrating a remarkable
accuracy rate of 96.50% in the precise prediction of PCOS disease.
