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Full Description
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. This book will illustrate advanced, innovative techniques/frameworks/concepts/machine learning methodologies, enhancing the future healthcare system. Combating Women's 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 age-related medical issues that women face. 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 machine learning/deep learning in healthcare scenarios, especially concerning women's health conditions.
Contents
1. Role of Machine Learning in Women's Health: A Review Analysis. 2. Predicting Anxiety, Depression and Stress in Women Using Machine Learning Algorithms. 3. Gender-based Analysis of the Impact of Cardiovascular Disease Using Machine Learning: A Comparative Analysis. 4. Lifestyle and Dietary Management Associated With Chronic Diseases in Women Using Deep Learning. 5. Gender Differences in Diabetes Care and Management using AI. 6. Prenatal Ultrasound Diagnosis Using Deep Learning Approaches. 7. Deep Convolutional Neural Network for the Prediction of Ovarian Cancer. 8. Risk Prediction and Diagnosis of Breast Cancer using ML Algorithms. 9. Comparative Analysis of Machine Learning Algorithms to Diagnose Polycystic Ovary Syndrome. 10. A Comparative Analysis of Machine Learning Approaches in Endometrial Cancer. 11. Machine Learning Algorithm-Based Early Prediction of Diabetes: A New Feature Selection Using Correlation Matrix with Heat Map. 12. Analyzing Factors for Improving Pregnancy Outcomes Using Machine Learning. 13. Future Consideration and Challenges in Women's Health Using AI.
