Bio-Inspired Optimization for Medical Data Mining

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 healthca...

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Bibliographic Details
Main Author: Srivastava, Sumit (-)
Other Authors: Anand, Abhineet, Kumar, Abhishek, Saini, Bhavna, Rathore, Pramod Singh
Format: eBook
Language:Inglés
Published: Newark : John Wiley & Sons, Incorporated 2024.
Edition:1st ed
Subjects:
Medical informatics > Data processing.
Data mining.
See on Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009841205406719
Table of Contents:
  • 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.

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