Bio-Inspired Optimization for Medical Data Mining

Name: Bio-Inspired Optimization for Medical Data Mining
Brand: Wiley-Scrivener
Price: 168.99 EUR
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Sumit Srivastava Abhineet Anand Abhishek Kumar Bhavna Saini Pramod Singh Rathore(Herausgeber*in)

Wiley-Scrivener (Verlag)

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Erschienen am 24. Juni 2024

336 Seiten

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Beschreibung

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.

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 Faculty of Engineering, Manipal University, Jaipur, Rajasthan, India, and is currently a Post-Doctoral Fellow in Ingenium Research Group Lab, Universidad De Castilla- La Mancha, Ciudad Real, Spain. He has more than 170 publications in peer-reviewed national and international journals and conferences.

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.

Weitere Details

Weitere Ausgaben

Inhalt

Cover
Series Page
Title Page
Copyright Page
Contents
Preface
Chapter 1 Bioinspired Algorithms: Opportunities and Challenges
1.1 Introduction
1.1.1 Definition and Significance of Bioinspired Algorithms
1.1.2 Overview of the Chapter
1.2 Bioinspired Principles and Algorithms
1.2.1 Evolutionary Algorithms
1.2.2 Swarm Intelligence Algorithms
1.2.3 Artificial Neural Networks
1.2.4 Other Bioinspired Algorithms
1.3 Opportunities of Bioinspired Algorithms
1.3.1 Solving Complex Optimization Problems
1.3.2 Robustness in Dealing With Uncertainty and Noise
1.3.3 Parallel and Distributed Computing
1.3.4 Application Areas and Success Stories
1.4 Challenges of Bioinspired Algorithms
1.4.1 Parameter Tuning and Algorithm Configuration
1.4.2 Lack of Theoretical Analysis and Understanding
1.4.3 Risk of Premature Convergence
1.4.4 Computational Cost for Large-Scale Problems
1.4.5 Ethical Considerations and Limitations
1.5 Prominent Bioinspired Algorithms
1.5.1 Genetic Algorithms
1.5.2 Particle Swarm Optimization
1.5.3 Ant Colony Optimization
1.5.4 Artificial Neural Networks
1.6 Applications of Bioinspired Algorithms
1.6.1 Optimization Problems
1.6.2 Pattern Recognition and Machine Learning
1.6.3 Swarm Robotics
1.6.4 Other Domains
1.7 Future Research Directions
1.7.1 Improving Efficiency and Scalability
1.7.2 Enhancing Interpretability and Explainability
1.7.3 Integration With Other Computational Techniques
1.7.4 Addressing Ethical Concerns
1.8 Conclusion
1.8.1 Summary of Key Points
1.8.2 Implications and Future Prospects of Bioinspired Algorithms
References
Chapter 2 Evaluation of Phytochemical Screening and In Vitro Antiurolithiatic Activity of Myristica fragrans by Titrimetry Method Using Machine Learning
2.1 Introduction
2.2 Methodology
2.2.1 Collection of Plant Material
2.2.2 Qualitative Analysis of Phytochemicals
2.2.3 Study of In Vitro Antiurolithiatic Activity Using Titrimetry Method
2.2.3.1 Preparation of Calcium Oxalate
2.2.3.2 Preparation of Semipermeable Membrane From Eggs
2.2.3.3 In Vitro Antiurolithiatic Test Using Titrimetry Method
2.3 Result and Discussion
2.3.1 In Vitro Antiurolithiatic Activity Test
2.3.2 Analysis of Dissolved Calcium Oxalate
2.4 Conclusion
References
Chapter 3 Parkinson's Disease Detection Using Voice and Speech- Systematic Literature Review
3.1 Introduction
3.2 Research Questions
3.3 Method
3.3.1 Search Strategy
3.3.2 Inclusion Criteria
3.3.3 Subprocesses Involved in PD Detection Process
3.3.4 Data Sets
3.3.4.1 Parkinson's Data Set-UCI Machine Learning Dataset
3.3.4.2 PC-GITA Dataset
3.3.4.3 mPower Dataset
3.3.4.4 Mobile Device Voice Recordings (MDVR-KCL) Dataset
3.3.4.5 Italian Parkinson's Voice and Speech (IPVS) Dataset
3.3.4.6 Parkinson Speech Dataset With Multiple Types of Sound Recordings Dataset
3.3.4.7 Parkinson's Telemonitoring Dataset
3.4 Algorithms
3.5 Features
3.5.1 Acoustic Features
3.5.1.1 Jitter (Local, Absolute)
3.5.1.2 Jitter (Local)
3.5.1.3 Jitter (rap)
3.5.1.4 Jitter (ppq5)
3.5.1.5 Shimmer (Local)
3.5.1.6 Shimmer (local, dB)
3.5.1.7 Shimmer (apq3)
3.5.1.8 Shimmer (apq5)
3.5.2 Spectogram-Based Methods
3.5.2.1 MFCC
3.6 Conclusion
References
Chapter 4 Tumor Detection and Classification
4.1 Introduction
4.2 Methods Used for Detection of Tumors
4.3 Methods Used for Classification of Tumours
4.3.1 Segmentation
4.3.2 Region Growing Method
4.3.3 Seeded Region Growing Method
4.3.4 Unseeded Region Growing Method
4.3.5 .-Connected Method
4.3.6 Threshold Based Method
4.3.7 K-Means Method
4.3.8 Watershed Method
4.3.9 Comparison of Different Segmentation Techniques Based on the Advantages and Disadvantages
4.3.10 Comparison of Different Segmentation Techniques Based on Accuracy
4.3.11 Comparison of Region Based and Threshold Based Segmentation Techniques Based on Different Parameters
4.4 Machine Learning
4.4.1 Supervised Learning
4.4.2 Unsupervised Learning
4.4.3 Reinforcement Learning
4.4.4 K-Nearest Neighbour (KNN)
4.4.5 Support Vector Machine (SVM)
4.4.6 Random Forest
4.5 Deep Learning (DL)
4.5.1 Convolutional Neural Networks (CNN)
4.5.1.1 Convolution Layer
4.5.1.2 Pooling Layer
4.5.1.3 Architecture of CNN
4.5.1.4 Comparison of Different Variations of CNN Techniques
4.5.2 Long Short-Term Memory (LSTM)
4.5.3 Artificial Neural Network (ANN)
4.5.4 Accuracy of Different Models Discussed Above
4.5.5 Accuracy of Other Different Techniques Being Used
4.6 Performance Metrics
4.6.1 Accuracy
4.6.2 Precision
4.6.3 Recall
4.6.4 Specificity
4.6.5 F1-Measure
4.7 Method Wise Trend of Using Techniques for Detection of Brain Tumor
4.8 Conclusion
References
Chapter 5 Advancements in Tumor Detection and Classification
5.1 Introduction
5.2 Imaging Techniques Used in Tumor Detection and Classification
5.2.1 X-Ray
5.2.2 CT Scan
5.2.3 MRI
5.2.4 Ultrasound
5.3 Molecular Biology Techniques
5.3.1 PCR
5.3.2 FISH
5.3.3 Next-Generation Sequencing
5.3.4 Western Blotting
5.4 Machine Learning and Artificial Intelligence
5.5 Tumor Classification
5.5.1 TNM Staging System
5.5.2 Histological Grading
5.5.3 Molecular Subtyping
5.6 Challenges and Future Directions
References
Chapter 6 Classification of Brain Tumor Using Machine Learning Techniques: A Comparative Study
6.1 Introduction
6.2 Related Work
6.3 Datasets
6.4 Experimental Setup
6.5 Results and Discussion
6.5.1 Evaluation Metrics
6.6 Conclusion
6.6.1 Significance of the Study
References
Chapter 7 Exploring the Potential of Dingo Optimizer: A Promising New Metaheuristic Approach
7.1 Introduction
7.2 Architecture of Dingo Optimizer
7.3 Initialization Process
7.3.1 Population Size
7.3.2 Dingo Population Initialization
7.3.3 Fitness Assessment
7.3.4 Best Dingo
7.3.5 Recordkeeping
7.4 Iteration Phase
7.6 Other Optimization Techniques
7.7 Conclusion
References
Chapter 8 Bioinspired Genetic Algorithm in Medical Applications
8.1 Introduction
8.2 The Genetic Algorithm
8.3 Radiology
8.4 Oncology
8.5 Endocrinology
8.6 Obstetrics and Gynecology
8.7 Pediatrics
8.8 Surgery
8.9 Infectious Diseases
8.10 Radiotherapy
8.11 Rehabilitation Medicine
8.12 Neurology
8.13 Health Care Management
8.14 Conclusion
References
Chapter 9 Artificial Immune System Algorithms for Optimizing Nanoparticle Design in Targeted Drug Delivery
9.1 Introduction
9.2 Artificial Immune Cells
9.3 The Artificial Immune System Architecture
References
Chapter 10 Diabetic Retinopathy Detection by Retinal Blood Vessel Segmentation and Classification Using Ensemble Model
10.1 Introduction
10.2 Literature Review
10.3 Proposed System
10.4 Conclusion and Future Scope
References
Chapter 11 Diabetes Prognosis Model Using Various Machine Learning Techniques
11.1 Introduction
11.1.1 Disease Identification
11.1.2 Data, Information, and Knowledge
11.1.3 Knowledge Discovery in Databases
11.1.4 Predictive Analytics
11.1.5 Supervised Learning and Machine Learning
11.1.6 Predictive Models
11.1.7 Data Validation and Cleaning
11.1.8 Discretization
11.2 Literature Review
11.2.1 Neural Networks
11.2.2 Trees
11.2.3 K-Nearest Neighbors
11.3 Proposed Model
11.3.1 Predictive Models in Health
11.4 Experimental Results and Discussion
11.4.1 Prediction of Diabetes with Artificial Neural Networks Supervised Learning Algorithms
11.4.2 Improving the Prediction Ratio of Diabetes Diagnoses Using Fuzzy Logic and Neural Networks
11.4.3 ARIC: Type 2 Diabetes Risk Predictive Model
11.4.4 Evaluation of Neural Network Algorithms for Prediction Models of Type 2 Diabetes
11.4.5 Reliable and Objective Recommendation System for the Diagnosis of Chronic Diseases
11.5 Conclusion
References
Chapter 12 Diagnosis of Neurological Disease Using Bioinspired Algorithms
12.1 Introduction
12.1.1 Neurological Diseases
12.1.2 Introduction to Bioinspired Algorithms
12.1.3 Types of Bioinspired Algorithms Commonly Used in Healthcare
12.1.4 Advantages and Limitations of Bioinspired Algorithms
12.1.5 Limitations
12.1.6 Applications of Bioinspired Algorithms in Healthcare
12.1.7 Benefits of Bioinspired Algorithms in Healthcare Over Traditional Approaches
12.2 Neurological Disease Diagnosis
12.2.1 Bioinspired Algorithms for Neurological Disease Diagnosis
12.2.2 Neural Networks in Neurological Disease Diagnosis
12.2.2.1 How NNs Can Be Trained Using Bioinspired Optimization Techniques
12.2.3 Other Bioinspired Algorithms in Neurological Disease Diagnosis
12.3 Challenges and Future Directions
12.4 Conclusion
References
Chapter 13 Optimizing Artificial Neural-Network Using Genetic Algorithm
13.1 Introduction
13.1.1 ANN
13.1.2 Genetic Algorithm
13.2 Methodology
13.2.1 Mathematical Working
13.3 Brief Study on Existing Implementations
13.3.1 Using Different Types of ANNs
13.3.2 Using MLPs
13.4 Comparative Study on Different Implementations
13.4.1 Conclusion
References
Chapter 14 Bioinspired Applications in the Medical Industry: A Case Study
14.1 Introduction
14.1.1 Background
14.1.2 Motivation
14.1.3 Research Objectives
14.2 Overview of Bioinspired Algorithms
14.2.1 Definition and Concepts
14.2.2 Types of Bioinspired Algorithms
14.2.3 Pros and Cons
14.2.3.1 Advantages of Bioinspired Algorithms
14.2.3.2 Limitations of Bioinspired Algorithms
14.3 Applications of Bioinspired Algorithms in Medical Field
14.4 Review of the Case Studies
14.5 Case Study
14.5.1 Problem Statement
14.5.2 Methodology
14.5.3 Data Collection, Acquisition, and Preprocessing
14.5.4 Feature Selection
14.5.5 Classification Algorithm Implementation
14.5.6 Experimental Results and Analysis
14.5.7 Discussion and Conclusion
14.6 Some Examples of the Case Studies Related to Medical Field and Can Be Solved with Bioinspired Algorithms
14.7 Future Directions and Recommendations for Future Research
14.8 Conclusion and Summary of Findings
References
Index
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