Hybrid PCOS net: A synergistic CNN-LSTM approach for accurate polycysti covary syndrome detection

AbstractPCOS, a common hormonal disorder in women of reproductive age, manifests with symptoms like irregular periods, ovarian cysts, and hormonal imbalances. Early diagnosis and intervention for PCOS are vital due to its link with metabolic and cardiovascular complications. It is essential for enhancing overall well-being and mitigating risks like infertility and metabolic issues. This paper is centered on the development of two distinct predictive models for PCOS detection, utilizing both image and text datasets. Ensemble learning methods like logistic regression, random forest, and support vector machines are employed to capitalize on the strengths of each dataset. The resultant combined model demonstrates an impressive accuracy of 89% and an area under the curve (AUC) score of 0.83. Additionally, the investigation explores advanced deep learning techniques by integrating Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to provide features for a Fully Connected Neural Network (FCNN). This innovative approach yields exceptional results, achieving an accuracy of 96.07% with minimal loss.