"The main focus of this book is the examination of health issues faced by women and the role of machine learning can play as a solution to these challenges. It will illustrate advanced, innovative techniques/frameworks/concepts/ methodologies of machine learning which will enhance the future healthcare system. Combating Female Health Issues with Machine Learning: Challenges and Solutions, examines the fundamental concepts and analysis of machine learning algorithms. The editors and authors of this book examine new approaches for different medical issues faced by women 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 womens infertility risk. Among the topics discussed are gender differences in Type2 diabetes care and its management as it relates to gender using artificial intelligence. The book also discusses advanced techniques to 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 womens health using artificial intelligence. This book is intended for medical researchers, healthcare technicians, scientists,, programmers, and graduate-level students interested looking to better understand and develop applications of ML/DL in healthcare scenarios, especially in relation to womens health conditions"--