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Advanced Machine Learning for Complex Medical Data Analysis

Editors: Saumendra Kumar Mohapatra, Mihir Narayan Mohanty, Rashmita Khilar

Advanced Machine Learning for Complex Medical Data Analysis

ISBN: 978-981-5313-39-0
eISBN: 978-981-5313-38-3 (Online)

Introduction

Advanced Machine Learning for Complex Medical Data Analysis is a definitive guide to leveraging machine learning to solve critical challenges in medical data analysis. This book discusses cutting-edge methodologies, from predictive modeling to neural networks, tailored to address the unique complexities of medical and healthcare data. It combines theoretical frameworks with practical applications, ensuring readers gain a comprehensive understanding of both concepts and real-world implementations.

The book covers diverse topics, including medical image denoising, the transformative role of GANs, IoT applications in healthcare, early disease detection using speech data, and COVID detection using autoencoders. It also explores the impact of big data, statistical approaches to medical analytics, and public health improvements through technology.

Key Features:

  • - Practical insights into deploying advanced machine learning models for healthcare.
  • - Real-world case studies on diverse diseases and datasets.
  • - Cutting-edge topics like explainable AI, federated learning, and ethical considerations.
  • - Methods for improving data accuracy, efficiency, and privacy.


Readership

Researchers, academics, graduate students, and professionals in data science, bioinformatics, and healthcare analytics.

Foreword

In the ever-evolving landscape of healthcare, the fusion of advanced machine learning and medical data analysis stands as a beacon of innovation and promise. As we navigate the complexities of a data-rich era, the book you hold in your hands, "Advanced Machine Learning for Complex Medical Data Analysis," emerges as a timely and indispensable guide to the forefront of this transformative intersection.

As our understanding of medical science deepens, so does our ability to harness the potential of machine learning algorithms. This edited volume, curated by Mihir Narayan Mohanty, Rashmita Khilar, and Saumendra Kumar Mohapatra and a cadre of esteemed contributors, brings together a tapestry of insights, methodologies, and breakthroughs that collectively define the state of the art in medical data analysis.

The chapters contained within these pages span the spectrum of applications, from predictive modelling that foretells patient outcomes to the nuanced intricacies of personalized medicine. The authors, each an expert in their field, share not only their successes but also the challenges and ethical considerations that accompany the integration of advanced machine learning into the fabric of healthcare.

What makes this volume truly exceptional is its ability to balance the theoretical underpinnings of machine learning with the practical implications for medical practitioners, researchers, and technologists. As we embark on a journey through these chapters, we are guided not only by the intricacies of algorithms but also by a commitment to improving patient outcomes, streamlining healthcare workflows, and enhancing the overall efficacy of medical decision-making.

To the readers, I encourage you to approach this book with a sense of curiosity and anticipation. The insights contained herein have the potential to shape the future of healthcare delivery, making it more precise, personalized, and responsive to the needs of individual patients and populations at large.

I extend my heartfelt congratulations to the editors and the contributors for their dedication to advancing the field. May this volume serve as a catalyst for continued exploration, collaboration, and innovation at the nexus of machine learning and medical data analysis.

Gopinath Palai
Faculty of Engineering and Technology, Sri Sri University, Cuttack, Odisha