Bio-Inspired Optimization for Medical Data Mining

This book is a comprehensive exploration of bio-inspired optimization techniques and their potential applications in healthcare.

Bio-Inspired Optimization for Medical Data Mining is a groundbreaking book that delves into the convergence of nature's ingenious algorithms and cutting-edge healthcare technology. Through a comprehensive exploration of state-of-the-art algorithms and practical case studies, readers gain unparalleled insights into optimizing medical data processing, enabling more precise diagnosis, optimizing treatment plans, and ultimately advancing the field of healthcare.

Organized into 15 chapters, readers learn about the theoretical foundation of pragmatic implementation strategies and actionable advice. In addition, it addresses current developments in molecular subtyping and how they can enhance clinical care. By bridging the gap between cutting-edge technology and critical healthcare challenges, this book is a pivotal contribution, providing a roadmap for leveraging nature-inspired algorithms.

In this book, the reader will discover

• Cutting-edge bio-inspired algorithms designed to optimize medical data processing, providing efficient and accurate solutions for complex healthcare challenges;
• How bio-inspired optimization can fine-tune diagnostic accuracy, leading to better patient outcomes and improved medical decision-making;
• How bio-inspired optimization propels healthcare into a new era, unlocking transformative solutions for medical data analysis;
• Practical insights and actionable advice on implementing bio-inspired optimization techniques and equipping effective real-world medical data scenarios;
• Compelling case studies illustrating how bio-inspired optimization has made a significant impact in the medical field, inspiring similar success stories.

Audience

This book is designed for a wide-ranging audience, including medical professionals, healthcare researchers, data scientists, and technology enthusiasts.

Sumit Srivastava, PhD, is the director of Information Technology at Manipal University, Jaipur, India. He obtained his doctorate in data mining from the University of Rajasthan, India. His areas of research involve algorithms, data science, knowledge, and engineering education. He has published more than 70 research papers in review journals.

Abhineet Anand, PhD, is a professor in computer science and engineering at Chandigarh University, Mohali, Punjab. He is also the director of the institution. His research includes artificial intelligence, machine learning, cloud computing, optical fiber, etc. He has published in various international journals and conferences, along with four book chapters.

Abhishek Kumar, PhD, is an associate professor in the Faculty of Engineering, Manipal University, Jaipur, Rajasthan, India, and is currently a Post-Doctoral Fellow in Ingenium Research Group Lab, Universidad De Castilla- La Mancha, Ciudad Real, Spain. He has more than 170 publications in peer-reviewed national and international journals and conferences.

Bhavna Saini, PhD, is an assistant professor at Central University, Rajasthan, India. His areas of research include face recognition and fingerprint recognition, data management systems, machine learning, and computer vision. He has numerous books and research papers at national and international levels.

Pramod Singh Rathore is an assistant professor in the Department of Computer and Communication Engineering, Manipal University Jaipur, India. He has teaching experience of more than 10 years and has 45 publications in peer-reviewed national and international journals.