Bio-Inspired Optimization for Medical Data Mining
1st Edition
Published on 24. September 2024
Book
Hardback
336 pages
978-1-394-21418-1 (ISBN)
Description
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.
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.
More details
Language
English
Place of publication
United States
Publishing group
John Wiley & Sons Inc
Target group
Professional and scholarly
Product notice
sewn/stitched
Cloth over boards
Weight
726 gr
ISBN-13
978-1-394-21418-1 (9781394214181)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Sumit Srivastava | Abhineet Anand | Abhishek Kumar
Bio-Inspired Optimization for Medical Data Mining
E-Book
07/2024
1st Edition
Wiley
€177.99
Available for download
Sumit Srivastava | Abhineet Anand | Abhishek Kumar
Bio-Inspired Optimization for Medical Data Mining
E-Book
06/2024
1st Edition
Wiley
€177.99
Available for download
Persons
Sumit Srivastava, PhD, is the director of Information Technology at Manipal University, Jaipur, India. He obtained his doctorate in data mining from the University of Rajasthan, India. His areas of research involve algorithms, data science, knowledge, and engineering education. He has published more than 70 research papers in review journals.
Abhineet Anand, PhD, is a professor in computer science and engineering at Chandigarh University, Mohali, Punjab. He is also the director of the institution. His research includes artificial intelligence, machine learning, cloud computing, optical fiber, etc. He has published in various international journals and conferences, along with four book chapters.
Abhishek Kumar, PhD, is an associate professor in the Computer Science & Engineering Department at Chandigarh University, Punjab, India, and is affiliated with the University of Castilla-La Mancha (UCLM), Toledo, Spain. His research areas include artificial intelligence, renewable energy, image processing, and machine learning. In total, he has more than 100 publications in peer-reviewed journals. Kumar is a keynote speaker and a member of various national and international societies in the field of engineering and research. He was awarded the CV Ramen National Award in 2018 in the young researcher and faculty category.
Bhavna Saini, PhD, is an assistant professor at Central University, Rajasthan, India. His areas of research include face recognition and fingerprint recognition, data management systems, machine learning, and computer vision. He has numerous books and research papers at national and international levels.
Pramod Singh Rathore is an assistant professor in the Department of Computer and Communication Engineering, Manipal University Jaipur, India. He has teaching experience of more than 10 years and has 45 publications in peer-reviewed national and international journals.
Abhineet Anand, PhD, is a professor in computer science and engineering at Chandigarh University, Mohali, Punjab. He is also the director of the institution. His research includes artificial intelligence, machine learning, cloud computing, optical fiber, etc. He has published in various international journals and conferences, along with four book chapters.
Abhishek Kumar, PhD, is an associate professor in the Computer Science & Engineering Department at Chandigarh University, Punjab, India, and is affiliated with the University of Castilla-La Mancha (UCLM), Toledo, Spain. His research areas include artificial intelligence, renewable energy, image processing, and machine learning. In total, he has more than 100 publications in peer-reviewed journals. Kumar is a keynote speaker and a member of various national and international societies in the field of engineering and research. He was awarded the CV Ramen National Award in 2018 in the young researcher and faculty category.
Bhavna Saini, PhD, is an assistant professor at Central University, Rajasthan, India. His areas of research include face recognition and fingerprint recognition, data management systems, machine learning, and computer vision. He has numerous books and research papers at national and international levels.
Pramod Singh Rathore is an assistant professor in the Department of Computer and Communication Engineering, Manipal University Jaipur, India. He has teaching experience of more than 10 years and has 45 publications in peer-reviewed national and international journals.
Content
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
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