Publisher's Synopsis
In the domain of women's health, the perimenopause to menopause transition stands out as a critical yet often misunderstood experience. Spanning several years before menopause, the perimenopause phase introduces a myriad of symptoms that impact a significant percentage of women globally. The distressing nature of these symptoms is underscored by the fact that while most subside within five years, a substantial minority endure them for up to 12 years. Comprehending and mitigating the impact of perimenopausal symptoms on women's health is an imperative challenge demanding a solution. Utilizing AI Techniques for the Perimenopause to Menopause Transition confronts the challenges faced by women during this pivotal period of change, employing cutting-edge deep learning approaches to identify, analyze, and address the associated symptoms. The book commences by elucidating the fundamental principles of perimenopause, providing readers with a robust foundation to comprehend the biological intricacies at play. Advanced machine learning techniques are then explored beyond conventional diagnostic methods, enabling a more nuanced identification and analysis of key menopausal symptoms. Statistical tools offer insights into global patterns of women's health. As methodologies are explored, the ethical landscape surrounding the collection of sensitive female health data is navigated. Addressing security and privacy concerns becomes paramount in the quest to harness AI for the betterment of women's health. Medical practitioners, healthcare providers, researchers, data scientists, and individuals experiencing perimenopause or menopause will find invaluable insights. Moreover, it holds significance for professionals in public health policies, educational institutions, and the pharmaceutical and health-tech industries.
