Bio-Inspired Optimization for Medical Data Mining

Sumit Srivastava, Abhineet Anand, Abhishek Kumar, Bhavna Saini, Pramod Singh Rathore

• Publisher: Wiley-Scrivener
• Year: 2024
• ISBN:
  9781394214204
• Edition: 1
• Topic: Informatik
• Subtopic: Programmieralgorithmus

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.

Table of contents

• Cover
• Series Page
• Title Page
• Copyright Page
• Contents
• Preface
• Chapter 1 Bioinspired Algorithms: Opportunities and Challenges
• Chapter 2 Evaluation of Phytochemical Screening and In Vitro Antiurolithiatic Activity of Myristica fragrans by Titrimetry Method Using Machine Learning
• Chapter 3 Parkinson’s Disease Detection Using Voice and Speech— Systematic Literature Review
• Chapter 4 Tumor Detection and Classification
• Chapter 5 Advancements in Tumor Detection and Classification
• Chapter 6 Classification of Brain Tumor Using Machine Learning Techniques: A Comparative Study
• Chapter 7 Exploring the Potential of Dingo Optimizer: A Promising New Metaheuristic Approach
• Chapter 8 Bioinspired Genetic Algorithm in Medical Applications
• Chapter 9 Artificial Immune System Algorithms for Optimizing Nanoparticle Design in Targeted Drug Delivery
• Chapter 10 Diabetic Retinopathy Detection by Retinal Blood Vessel Segmentation and Classification Using Ensemble Model
• Chapter 11 Diabetes Prognosis Model Using Various Machine Learning Techniques
• Chapter 12 Diagnosis of Neurological Disease Using Bioinspired Algorithms
• Chapter 13 Optimizing Artificial Neural-Network Using Genetic Algorithm
• Chapter 14 Bioinspired Applications in the Medical Industry: A Case Study
• Index
• EULA