Combating Women's Health Issues with Machine Learning

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.

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.