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.

Table of Contents:
Preface xv 1 Bioinspired Algorithms: Opportunities and Challenges 1 Shweta Agarwal, Neetu Rani and Amit Vajpayee 1.1 Introduction 2 1.2 Bioinspired Principles and Algorithms 3 1.3 Opportunities of Bioinspired Algorithms 7 1.4 Challenges of Bioinspired Algorithms 9 1.5 Prominent Bioinspired Algorithms 12 1.6 Applications of Bioinspired Algorithms 18 1.7 Future Research Directions 21 1.8 Conclusion 23 2 Evaluation of Phytochemical Screening and In Vitro Antiurolithiatic Activity of Myristica fragrans by Titrimetry Method Using Machine Learning 31 G. Lalitha, S. Surya and M.P. Karthikeyan 2.1 Introduction 32 2.2 Methodology 33 2.3 Result and Discussion 35 2.4 Conclusion 38 3 Parkinson's Disease Detection Using Voice and Speech--Systematic Literature Review 41 Ronak Khatwad, Suyash Tiwari, Yash Tripathi, Ajay Nehra and Ashish Sharma 3.1 Introduction 42 3.2 Research Questions 43 3.3 Method 44 3.4 Algorithms 60 3.5 Features 63 3.6 Conclusion 67 4 Tumor Detection and Classification 75 Hermehar P.S. Bedi, Sukhpreet Kaur and Saumya Rajvanshi 4.1 Introduction 76 4.2 Methods Used for Detection of Tumors 77 4.3 Methods Used for Classification of Tumours 80 4.4 Machine Learning 84 4.5 Deep Learning (DL) 89 4.6 Performance Metrics 95 4.7 Method Wise Trend of Using Techniques for Detection of Brain Tumor 97 4.8 Conclusion 97 5 Advancements in Tumor Detection and Classification 103 Mayank Puri, Aman Garg and Lekha Rani 5.1 Introduction 104 5.2 Imaging Techniques Used in Tumor Detection and Classification 105 5.3 Molecular Biology Techniques 111 5.4 Machine Learning and Artificial Intelligence 115 5.5 Tumor Classification 121 5.6 Challenges and Future Directions 125 6 Classification of Brain Tumor Using Machine Learning Techniques: A Comparative Study 129 Gandla Shivakanth, Bhaskar Marapelli, A. Shivakumar Reddy, Dasari Manasa and Samtha Konda 6.1 Introduction 130 6.2 Related Work 131 6.3 Datasets 132 6.4 Experimental Setup 133 6.5 Results and Discussion 134 6.6 Conclusion 136 7 Exploring the Potential of Dingo Optimizer: A Promising New Metaheuristic Approach 141 Anju Yadav and Vivek Kumar Varma 7.1 Introduction 141 7.2 Architecture of Dingo Optimizer 142 7.3 Initialization Process 144 7.4 Iteration Phase 148 7.6 Other Optimization Techniques 150 7.7 Conclusion 151 8 Bioinspired Genetic Algorithm in Medical Applications 155 Krati Taksali, Arpit Kumar Sharma and Manish Rai 8.1 Introduction 156 8.2 The Genetic Algorithm 157 8.3 Radiology 158 8.4 Oncology 160 8.5 Endocrinology 161 8.6 Obstetrics and Gynecology 162 8.7 Pediatrics 162 8.8 Surgery 163 8.9 Infectious Diseases 164 8.10 Radiotherapy 164 8.11 Rehabilitation Medicine 165 8.12 Neurology 165 8.13 Health Care Management 166 8.14 Conclusion 166 9 Artificial Immune System Algorithms for Optimizing Nanoparticle Design in Targeted Drug Delivery 169 Ashish Kumar and Vivek Verma 9.1 Introduction 170 9.2 Artificial Immune Cells 171 9.3 The Artificial Immune System Architecture 172 10 Diabetic Retinopathy Detection by Retinal Blood Vessel Segmentation and Classification Using Ensemble Model 185 Gandla Shivakanth, K. Aruna Bhaskar, Bechoo Lal, A. Shivakumar Reddy and D. Manasa 10.1 Introduction 186 10.2 Literature Review 187 10.3 Proposed System 188 10.4 Conclusion and Future Scope 198 11 Diabetes Prognosis Model Using Various Machine Learning Techniques 201 Pawan Kumar Patidar, Manish Bhardwaj and Sumit Kumar 11.1 Introduction 202 11.2 Literature Review 209 11.3 Proposed Model 211 11.4 Experimental Results and Discussion 213 11.5 Conclusion 222 12 Diagnosis of Neurological Disease Using Bioinspired Algorithms 227 Inam Ul Haq 12.1 Introduction 228 12.2 Neurological Disease Diagnosis 244 12.3 Challenges and Future Directions 260 12.4 Conclusion 264 13 Optimizing Artificial Neural-Network Using Genetic Algorithm 269 Bhavy Pratap and Sulabh Bansal 13.1 Introduction 270 13.2 Methodology 278 13.3 Brief Study on Existing Implementations 283 13.4 Comparative Study on Different Implementations 285 14 Bioinspired Applications in the Medical Industry: A Case Study 289 Alankrita Aggarwal and Mohit Lalit 14.1 Introduction 290 14.2 Overview of Bioinspired Algorithms 291 14.3 Applications of Bioinspired Algorithms in Medical Field 296 14.4 Review of the Case Studies 297 14.5 Case Study 297 14.6 Some Examples of the Case Studies Related to Medical Field and Can Be Solved with Bioinspired Algorithms 300 14.7 Future Directions and Recommendations for Future Research 302 14.8 Conclusion and Summary of Findings 306 References 307 Index 309