Soft computing applications and techniques in healthcare / edited by Ashish Mishra, G. Suseendran and Trung-Nghia Phung.

Format:

Book

Contributor:

Mishra, Ashish (Ashish Kumar), editor.

Suseendran, G., editor.

Phung, Trung-Nghia, editor.

Series:

Information technology, management and operations research practices (Series)

Language:

English

Subjects (All):

Medical care--Technological innovations.

Medical care.

Physical Description:

1 online resource (277 pages).

Edition:

1st ed.

Place of Publication:

Boca Raton, Florida ; London ; New York : CRC Press, [2021]

Summary:

"This book provides insights into contemporary issues and challenges in soft computing applications and techniques in healthcare"-- Provided by publisher.

Contents:

Cover

Half Title

Series Page

Title Page

Copyright Page

Contents

Preface

Acknowledgements

Editors

Contributors

Chapter 1: Analytical Approach to Genetics of Cancer Therapeutics through Machine Learning

1.1. Introduction

1.2. Literature Review

1.3. Data Collection and Processing

1.4. Classification and Model Evaluation

1.4.1. K-Nearest Neighbours

1.4.2. Support Vector Machine

1.4.3. Kernels in Support Vector Machine

1.4.4. ADABoost

1.4.5. Random Forest

1.5. Logistic Regression

1.5.1. Naive Bayes

1.6. Results

1.7. Conclusion

References

Chapter 2: A Study on Behaviour of Neural Gas on Images and Artificial Neural Network in Healthcare

2.1. Introduction to Neural Gas

2.2. Neural Network in Healthcare

2.2.1. Current Medical Applications for ANNs

2.2.1.1. Disease Identification and Diagnosis

2.2.1.2. Personalised Medicine

2.2.1.3. Drug Discovery and Manufacturing

2.2.1.4. Predicting and Managing Epidemic Outbreaks

2.3. An Introduction to Self-Organising Maps

2.4. Related Work on Neural Gas

2.5. Proposed Work

2.5.1. Neural Gas Approach

2.5.1.1. Algorithm

2.5.2. Properties of Neural Gas

2.6. Experimental Results

2.6.1. Results and Graphs on Different Values of Epochs Parameter

2.6.1.1. Result and Graph for Animal Image [A]

2.6.1.2. Result and Graph for Building Image [B]

2.6.1.3. Result and Graph for Cloud Image [C]

2.6.1.4. Result and Graph for Flower Image [F]

2.6.1.5. Result and Graph on Vehicle Image [V]

2.7. Conclusion for Maximum and Minimum Differences

2.7.1. Conclusion Graph for Maximum Differences

2.7.1.1. Conclusion Graph for Maximum Differences for Epochs' Values

2.7.1.2. Conclusion Graph for Maximum Differences for Delta Values.

2.7.1.3. Conclusion Graph for Maximum Differences for Iteration (t) Values

2.7.1.4. Conclusion Graph for Maximum Differences for Alpha0 Values

2.7.1.5. Conclusion Graph for Maximum Differences for Alphaf Values

2.7.1.6. Conclusion Graph for Maximum Differences for Lambda0 Values

2.7.1.7. Conclusion Graph for Maximum Differences for Lambdaf Values

2.7.2. Conclusion Graph for Minimum Differences

2.7.2.1. Conclusion Graph for Minimum Differences for Epochs' Values

2.7.2.2. Conclusion Graph for Minimum Differences for Delta Values

2.7.2.3. Conclusion Graph for Minimum Differences for Iteration (t) Values

2.7.2.4. Conclusion Graph for Minimum Differences for Alpha0 Values

2.7.2.5. Conclusion Graph for Minimum Differences for Alphaf Values

2.7.2.6. Conclusion Graph for Minimum Differences for Lambda0 Values

2.7.2.7. Conclusion Graph for Minimum Differences for Lambdaf Values

2.8. Conclusion

Chapter 3: A New Approach for Parkinson's Disease Imaging Diagnosis Using Digitized Spiral Drawing

3.1. Introduction

3.1.1. Digital Spiral Basics

3.1.2. Text Organisation

3.2. Existing Methods and Experiment Performed

3.2.1. Related Work from the References

3.2.2. Performed Work

3.2.2.1. Data Acquisition and Collection

3.2.2.2. Feature Engineering

3.2.2.3. Model Selection and Parameter Tuning

3.2.2.4. Experimental Setup

3.3. Results

3.3.1. Metrics

3.4. Discussion

3.5. Conclusion

Chapter 4: Modelling and Analysis for Cancer Model with Caputo to Atangana-Baleanu Derivative

4.1. Introduction

4.2. Some Useful Definitions of Fractional Differential Operators

4.3. Cancer Model

4.3.1. Existence of Solution

4.3.2. AB Derivative for Mathematical Cancer Model.

4.3.3. Numerical Solution for Mathematical Model Having Kernel Mittag-Leffler

4.4. Conclusions

Chapter 5: Selection of Hospital Using Integrated Fuzzy AHP and Fuzzy TOPSIS Method

5.1. Introduction

5.2. Preliminaries of Fuzzy Sets and Fuzzy Numbers

5.3. Analytic Hierarchy Process (AHP)

5.4. Fuzzy Analytic Hierarchy Process (FAHP)

5.4.1. Chang's Extent Analysis of FAHP

5.5. Topsis Method

5.6. Selection Criteria of Hospital

5.7. Evaluation Framework

5.7.1. Implication of Fuzzy AHP

5.7.2. Implementation of TOPSIS

5.8. Conclusion

Chapter 6: Computation of Threshold Rate for the Spread of HIV in a Mobile Heterosexual Population and Its Implication for SIR Model in Healthcare

6.1. Introduction

6.2. Basic Concepts

6.2.1. Limit Cycle

6.2.2. Bendixson's Criterion

6.2.3. Poincare Bendixons

6.3. Basic Reproduction Number (R0)

6.4. The Threshold Rate

6.5. Description of the Model

6.6. Analysis of the Behaviour of the HIV-SIR Model

6.7. The Spread of HIV within the Population

6.8. The Rate of Removal of HIV Infection

6.9. Analysis of Local Asymptotic Stability

6.10. Numerical Simulation

6.11. Conclusion

Chapter 7: Application of Soft Computing Techniques to Heart Sound Classification: A Review of the Decade

7.1. Introduction

7.2. Related Literature Review

7.3. Steps for Heart Sound Classification

7.3.1. Pre-Processing

7.3.2. Feature Extraction

7.3.3. Classification

7.4. Research Gap

7.5. Conclusion

Chapter 8: Fuzzy Systems in Medicine and Healthcare: Need, Challenges and Applications

8.1. Introduction

8.1.1. Introduction to Fuzzy Logic Systems

8.1.2. Fuzzy Set Theory

8.1.3. Applications of Fuzzy Systems in Healthcare.

8.2. Fuzzy Systems in Healthcare

8.2.1. Challenges to Healthcare

8.2.2. Soft Computing Techniques Involved with Fuzzy Systems

8.2.2.1. Fuzzy Cognitive Maps

8.2.2.2. Neuro-Fuzzy Systems

8.2.2.3. Genetic Algorithms

8.2.2.4. Bayesian Networks

8.2.3. Fuzzy Logic in Medical Information Management

8.2.3.1. Medical Databases

8.2.3.2. Information Retrieval

8.3. Role of Fuzzy Systems in Diagnosis and Risk to Health Analysis

8.3.1. Lung Cancer

8.3.2. Heterogeneous Childhood Cancers

8.3.2.1. Breast Cancer

8.3.2.2. Diagnosis of Diseases

8.3.2.3. Periodontal Dental Disease

8.3.2.4. Anaemia

8.3.2.5. HIV

8.3.2.6. Malaria

8.3.2.7. Diabetes

8.3.2.8. Asthma

8.3.3. Monitoring Patient's Condition during Heart Surgery

8.4. Role of Fuzzy Systems in Medicine

8.4.1. Analysis of Signs or Symptoms

8.4.2. Medical Image Processing

8.4.3. Administration of Anaesthesia

8.4.4. Determination of Drug Dose

8.4.5. Improving Service Quality

8.4.6. Service Quality Evaluation in Yoncali Physiotherapy and Rehabilitation Hospital-Case Study

8.5. Conclusion

Chapter 9: Appliance of Machine Learning Algorithms in Prudent Clinical Decision-Making Systems in the Healthcare Industry

9.1. Introduction

9.1.1. Artificial Intelligence

9.1.2. Healthcare Industry

9.2. Computers and Healthcare Supervision in New Era

9.2.1. Medication

9.2.2. Patient Diagnosis

9.2.3. Telemedicine

9.2.4. Surgical Procedures

9.2.5. Communication and Sharing Information

9.2.6. Medical Imaging

9.3. Importance and Emergence of Machine Learning

9.3.1. Supervised Learning

9.3.2. Unsupervised Learning

9.3.3. Reinforcement Learning

9.3.4. How Machine Learning Has Changed Health Aspects

9.4. Automatic Drug Discovery

9.4.1. Stages Involved in Drug Discovery.

9.5. Feature Selection in Medicine

9.5.1. Attribute Ranker

9.5.2. Principal Component Analysis

9.5.3. Factor Analysis

9.5.4. Median Imputation

9.6. Regression Analysis in Medicine

9.6.1. Application of Regression in Medicine

9.7. Querying in Medicine

9.8. Density Estimation in Medicine

9.9. Dimension Reduction in Medicine

9.9.1. Feature Evaluator and Feature Ranker Algorithm

9.9.2. Linear DR Algorithms

9.9.3. Nonlinear DR Algorithms

9.9.4. Clustering Algorithms

9.10. Testing and Matching in Medicine

9.10.1. Diagnostic

9.10.2. Screening

9.10.3. Monitoring

9.10.4. Precision or Accuracy Aspects

9.11. Classification and Clustering in Medicine

9.11.1. Clustering

9.11.1.1. Hierarchical Clustering

9.11.1.2. Partition Methods

9.11.1.3. Density-Based Clustering

9.11.1.4. K-Nearest Neighbour

9.11.2. Classification

9.11.2.1. Texture Classification

9.11.2.2. Neural Networks

9.11.2.3. Data Mining

9.12. Conclusion

Chapter 10: Technique of Receiving Data from Medical Devices to Create Electronic Medical Records Database

10.1. Introduction

10.2. Automatic Technique of Acquiring Analog Electronic Medical Images

10.2.1. Introduction and Classification

10.2.2. Automatic Acquisition of Analog Electronic Medical Image from Imaging Equipment with Standard Communication Interface

10.2.3. Evaluation of the Quality of Electronic Medical Image after Acquisition

10.3. Automatic Technique of Acquiring Digital Electronic Medical Image

10.3.1. Introduction and Classification

10.3.2. Automated Technique of Digital Image Acquisition According to DICOM Standard

10.3.3. Evaluation of the Quality of Image Data after Acquisition

10.4. Automatic Technique of Acquiring Medical Data in Text.

10.4.1. Technical Data Analysis at the Output of Medical Equipment.

Notes:

Includes index.

Description based on print version record.

ISBN:

1-00-300349-4

1-003-00349-4

1-000-19106-0

9781003003496