Abstract
Deep learning and other kinds of artificial intelligence have shown significant promise in the realm of medicine. Identifying the signs and markers of female perimenopause is one of these applications. By merging deep learning approaches with interpretability and ethical issues, this study contributes to the increasing corpus of research on explainable artificial intelligence models. This project is testing a range of models, including support vector machines (SVM), random forest, deep learning, and logistic regression, to see which artificial intelligence model is most beneficial in diagnosing perimenopausal symptoms. To examine the practicability of these models, a range of measures such as recall, accuracy, precision, F1-score, and ethical judgements are utilised. In terms of recall, accuracy, and precision, the proposed enhanced hybrid deep learning strategy uses deep neural networks and attention processes to outperform traditional models. The study also stresses the importance of ethical considerations while developing AI models, particularly when it comes to data protection, privacy legislation, and preventing prejudice.