Combating Women's Health Issues with Machine Learning: Challenges and Solutions

The main focus of this book is the examination of women's health issues and the role machine learning can play as a solution to these challenges. It will illustrate advanced, innovative techniques/frameworks/concepts/machine learning methodologies, enhancing the future healthcare system. Combating Female Health Issues with Machine Learning: Challenges and Solutions examines machine learning algorithms' fundamental concepts and analysis.

The editors and authors of this book examine new approaches for different medical issues women face as they relate to age. Topics range from diagnosing diseases such as breast and ovarian cancer to using deep learning in prenatal ultrasound diagnosis. The authors also examine the best machine learning classifier for constructing the most accurate predictive model for women's infertility risk. Among the topics discussed are gender differences in type 2 diabetes care and its management as it relates to gender using artificial intelligence. The book also discusses advanced techniques for evaluating and managing cardiovascular disease symptoms, which are more common in women but often overlooked or misdiagnosed by many healthcare providers.

The book concludes by presenting future considerations and challenges in the field of women's health using artificial intelligence. This book is intended for medical researchers, healthcare technicians, scientists, programmers, and graduate-level students looking to understand better and develop applications of ML/DL in healthcare scenarios, especially concerning women's health conditions.