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. Bioinspired algorithms, also known as nature-inspired algorithms or evolutionary computation, are computational techniques that draw inspiration from the principles, behaviors, and mechanisms observed in biological systems. These algorithms mimic the adaptive and problem-­solving abilities found in nature to tackle complex optimization problems. By emulating the evolutionary processes, swarm behaviors, neural networks, or other biological phenomena, bioinspired algorithms offer innovative and efficient problem-solving approaches. Bioinspired algorithms have gained significant importance in various fields due to their ability to handle complex and challenging problems. The application areas of these algorithms are optimization, Machine Learning, robotics, data mining, and pattern recognition.