Advanced Machine Learning for Complex Medical Data Analysis.

Format:

Book

Author/Creator:

Mohapatra, Saumendra Kumar.

Contributor:

Mohanty, Mihir Narayan.

Khilar, Rashmita.

Language:

English

Subjects (All):

Machine learning.

Medical informatics.

Physical Description:

1 online resource (214 pages)

Edition:

1st ed.

Place of Publication:

Sharjah : Bentham Science Publishers, 2025.

Summary:

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.

Contents:

Cover

Title

Copyright

End User License Agreement

Contents

Foreword

Preface

List of Contributors

Computational Intelligence Approaches to Predictive Modeling in Clinical Dataset Issues and Challenges: A Review

Shweta Kharya1,*, Sunita Soni1, Tripti Swarnkar2, Santosh Kumar Sar3 and Sachi Nandan Mohanty4

INTRODUCTION

SIGNIFICANCE OF PREDICTIVE MODELING IN THE CLINICAL CARE INTELLIGENCE

Clinical Data Challenges

Predictive Modeling and Evaluation of Clinical Dataset

Evaluation & Validation Phase of Models

COMPUTATIONAL INTELLIGENCE APPROACHES FOR PREDICTIVE MODELING IN THE CLINICAL DATASET

Artificial Neural Network

Artificial Neural Network on a Clinical Dataset

Advantages

Disadvantages

Fuzzy Logic Approaches

Fuzzy Logic Approaches to a Clinical Dataset

Support Vector Machines Approaches

Support Vector Machines in Clinical Dataset

Decision Tree

Decision Tree approaches in the Clinical Dataset Generated by AI.

Notes:

Description based on publisher supplied metadata and other sources.

Part of the metadata in this record was created by AI, based on the text of the resource.

ISBN:

981-5313-38-X

OCLC:

1520590240