1st Edition
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. 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.
Ritika Arora, Sharad Chauhan and Harpreet Kaur
2. Predicting Anxiety, Depression and Stress in Women Using Machine Learning Algorithms
Kalpana Katiyar, Hera Fatma and Simran Singh
3. Gender-based Analysis of the Impact of Cardiovascular Disease Using Machine Learning: A Comparative Analysis
Ajit Kumar Varma, Arpita Choudhary and Priti Tagde
4. Lifestyle and Dietary Management Associated with Chronic Diseases in Women Using Deep Learning
Rajdeep Kaur, Rakesh Kumar and Meenu Gupta
5. Gender Differences in Diabetes Care and Management Using AI
Pawan Whig, Shama Kouser, Tabrej Ahamad Khan, Syed Ali Mehdi, Naved Alam and Rahul Reddy Nadikattu
6. Prenatal Ultrasound Diagnosis Using Deep Learning Approaches
P. Nagaraja, S. Lakshmanan, P.Shanmugavadivu and M. Mary Shanthi Rani.
7. Deep Convolutional Neural Network for the Prediction of Ovarian Cancer
S.Lakshmanan, P.Nagaraja, M.Mary Shanthi Rani and P. Shanmugavadivu
8. Risk Prediction and Diagnosis of Breast Cancer Using ML Algorithms.
Neeru Saxena, Rashmi Vaishnav, Surya Saxena and Umesh Kumar
9. Comparative Analysis of Machine Learning Algorithms to Diagnose Polycystic Ovary Syndrome
Arpit Raj, Poonam Joshi, Sarika Devi and Sapna Rawat.
10. A Comparative Analysis of Machine Learning Approaches in Endometrial Cancer
Chaitanya Pandey, Nitya Nagpal, Rahul Khurana and Preeti Nagrath
11. Machine Learning Algorithm-Based Early Prediction of Diabetes: A New Feature Selection Using Correlation Matrix with Heat Map
Salliah Shafi Bhat and Gufran Ahmad Ansari
12. Analyzing Factors for Improving Pregnancy Outcomes Using Machine Learning.
Sarika Devi, Arpit Raj, Poonam Joshi and Sapna Rawat
13. Future Consideration and Challenges in Women's Health Using AI
C. Prakash, L. P. Singh, A Gupta, R Kumar and A Bhardwaj.
Biography
Meenu Gupta is Associate Professor in the UIE-CSE Department at Chandigarh University, India. She completed her PhD in Computer Science and Engineering with an emphasis on Traffic Accident Severity Problems from Ansal University, India, in 2020. She has more than 14 years of teaching experience. Her research areas cover machine learning, intelligent systems and data mining, with a specific interest in artificial intelligence, image processing and analysis, smart cities, data analysis and human/brain–machine interaction (BMI). She has edited five books and authored four engineering books. She reviews several journals, including Big Data, CMC, Scientific Reports and TSP. She is a life member of ISTE and IAENG. She has authored or co-authored more than 30 book chapters and over 80 papers in refereed international journals and conferences.
D. Jude Hemanth is Associate Professor in the Department of ECE at Karunya University, India. He also holds the “Visiting Professor” position in the Faculty of Electrical Engineering and Information Technology at the University of Oradea, Romania. He received his BE degree in ECE from Bharathiar University, India, in 2002, his ME degree in Communication Systems from Anna University, India, in 2006, and his PhD from Karunya University, India, in 2013. His research areas include computational intelligence and image processing, communication systems, biomedical engineering, robotics and healthcare, computational intelligence and information systems, and artificial intelligence. He is also an editor of the Neuroscience Informatics Journal.
