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E-grāmata: Swarm Intelligence and Evolutionary Algorithms in Healthcare and Drug Development

Edited by (Duy Tan University, Vietnam), Edited by (Kyungpook National University, Korea (Rep.)), Edited by (CHRIST (Deemed to be University), India)
  • Formāts: 168 pages
  • Izdošanas datums: 11-Nov-2019
  • Izdevniecība: Chapman & Hall/CRC
  • Valoda: eng
  • ISBN-13: 9781000727036
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Swarm Intelligence and Evolutionary Algorithms in Healthcare and Drug DevelopmentPalielināt
  • Formāts: 168 pages
  • Izdošanas datums: 11-Nov-2019
  • Izdevniecība: Chapman & Hall/CRC
  • Valoda: eng
  • ISBN-13: 9781000727036
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Healthcare sector is characterized by difficulty, dynamism and variety. In 21st century, healthcare domain is surrounded by tons of challenges in terms of Disease detection, prevention, high costs, skilled technicians and better infrastructure. In order to handle these challenges, Intelligent Healthcare management technologies are required to play an effective role in improvising patients life. Healthcare organizations also need to continuously discover useful and actionable knowledge to gain insight from tons of data for various purposes for saving lives, reducing medical operations errors, enhancing efficiency, reducing costs and making the whole world a healthy world.

Applying Swarm Intelligence and Evolutionary Algorithms in Healthcare and Drug Development is essential nowadays. The objective of this book is to highlight various Swarm Intelligence and Evolutionary Algorithms techniques for various medical issues in terms of Cancer Diagnosis, Brain Tumor, Diabetic Retinopathy, Heart disease as well as drug design and development. The book will act as one-stop reference for readers to think and explore Swarm Intelligence and Evolutionary Algorithms seriously for real-time patient diagnosis, as the book provides solutions to various complex diseases found critical for medical practitioners to diagnose in real-world.

Key Features:











Highlights the importance and applications of Swarm Intelligence and Evolutionary Algorithms in Healthcare industry.





Elaborates Swarm Intelligence and Evolutionary Algorithms for Cancer Detection.





In-depth coverage of computational methodologies, approaches and techniques based on Swarm Intelligence and Evolutionary Algorithms for detecting Brain Tumour including deep learning to optimize brain tumor diagnosis.





Provides a strong foundation for Diabetic Retinopathy detection using Swarm and Evolutionary algorithms.





Focuses on applying Swarm Intelligence and Evolutionary Algorithms for Heart Disease detection and diagnosis.





Comprehensively covers the role of Swarm Intelligence and Evolutionary Algorithms for Drug Design and Discovery.

The book will play a significant role for Researchers, Medical Practitioners, Healthcare Professionals and Industrial Healthcare Research and Development wings to conduct advanced research in Healthcare using Swarm Intelligence and Evolutionary Algorithms techniques.
Preface xi
About the Editors xv
Contributors xix
Abbreviations xxi
Chapter 1 Swarm Intelligence and Evolutionary Algorithms in Disease Diagnosis--Introductory Aspects
1(140)
Bhushan Inje
Sandeep Kumar
Anasd Nayyar
1.1 Introduction
1(1)
1.2 Terminologies
2(5)
1.2.1 Swarm Intelligence
2(1)
1.2.1.1 Merits of Swarm Intelligence
3(1)
1.2.1.2 Classifications and Terminology
4(1)
1.2.2 Evolutionary Computation
5(1)
1.2.3 Evolutionary Computation Paradigms
6(1)
1.3 Importance Of Swarm Intelligence In Disease Diagnosis
7(3)
1.4 Importance Of Evolutionary Algorithms In Disease Diagnosis
10(4)
1.5 Conclusion
14(5)
Chapter 2 Swarm Intelligence and Evolutionary Algorithms for Cancer Diagnosis
19(26)
Bandana Mahapatra
Anand Nayyar
2.1 Introduction
19(2)
2.2 Classification of Cancer
21(5)
2.3 Challenges in Cancer Diagnosis
26(2)
2.3.1 Methods of Cancer Detection
26(1)
2.3.2 Issues and Challenges Faced While Cancer Detection Process
27(1)
2.4 Applying Swarm Intelligence Algorithm For Cancer Diagnosis
28(6)
2.4.1 SI Algorithms for Detection of Lung Cancer
29(1)
2.4.2 Swarm Intelligence for Breast Cancer
30(1)
2.4.3 Swarm Intelligence for Ovarian Cancer
30(1)
2.4.4 SI Algorithm for Early Detection of Gastro Cancer
30(1)
2.4.5 Swarm Intelligence for Treating Nano-Robots
31(3)
2.5 Applying Evolutionary Algorithm For Cancer Detection
34(6)
2.6 Conclusion
40(5)
Chapter 3 Brain Tumour Diagnosis
45(20)
Dhananiay Joshi
Nitin Choubey
Raiani Kumari
3.1 Introduction
45(5)
3.2 Applying Evolutionary Algorithms for Brain Tumor Diagnosis
50(4)
3.2.1 Evolutionary Algorithm
50(2)
3.2.2 Conceptual Framework 1: Applying Evolutionary Algorithm for Brain Tumor Diagnosis
52(2)
3.3 Applying Swarm Intelligence Algorithms For Brain Tumor Diagnosis
54(4)
3.3.1 Swarm Intelligence (SI) - Based Algorithms
54(1)
3.3.2 Self-Organization
55(1)
3.3.3 Division of Labor
55(1)
3.3.4 Particle Swarm Optimization
55(1)
3.3.5 Particle Swarm Optimization Algorithm
56(1)
3.3.6 Conceptual Framework 2: Applying Swarm Intelligence Based Algorithm for Brain Tumor Diagnosis
57(1)
3.4 Applying Swarm Intelligence And Evolutionary Algorithms Together for Diagnosis of Brain Tumor
58(1)
3.5 Applying Swarm Intelligence, Evolutionary Algorithm and Incorporating Topological Data Analysis (TDA) for Brain Tumor Diagnosis
59(1)
3.5.1 Topological Data Analysis
59(1)
3.6 Conclusion
59(6)
Chapter 4 Swarm Intelligence and Evolutionary Algorithms for Diabetic Retinopathy Detection
65(28)
Sachin Bhandari
Radhakrishna Rambola
Rajani Kumari
4.1 Introduction
65(7)
4.1.1 Classification of Diabetic Retinopathy
66(3)
4.1.2 Swarm Optimization and Evolutionary Algorithms
69(2)
4.1.3 Objectives and Contributions
71(1)
4.2 Feature of Diabetic Retinopathy
72(2)
4.2.1 Microaneurysms
72(1)
4.2.2 Haemorrhages
73(1)
4.2.3 Hard Exudates
73(1)
4.2.4 Soft Exudates
73(1)
4.2.5 Neo-Vascularization
74(1)
4.2.6 Macular Edema
74(1)
4.3 Detection of Diabetic Retinopathy By Applying Swarm Intelligence And Evolutionary Algorithms
74(13)
4.3.1 Genetic Algorithm
75(4)
4.3.2 Particle Swarm Optimization
79(2)
4.3.3 Ant Colony Optimization
81(3)
4.3.4 Cuckoo Search
84(1)
4.3.5 Bee Colony Optimization
85(2)
4.4 Conclusion
87(6)
Chapter 5 Swarm Intelligence and Evolutionary Algorithms for Heart Disease Diagnosis
93(24)
Raialakshmi Krishnamurthi
5.1 Introduction
93(2)
5.2 Prediction and Classification of Heart Disease Using Machine Learning/Swarm Intelligence
95(3)
5.2.1 Decision Support System
95(1)
5.2.2 Clinical Decision Support System
96(1)
5.2.3 Heart Disease Datasets
97(1)
5.3 Predicting Heart Attacks in Patients Using Artificial Intelligence Methods (Fuzzy Logic)
98(5)
5.3.1 Fuzzy Logic Approach for Heart Disease Diagnosis
99(2)
5.3.2 Fuzzy Rule Base
101(1)
5.3.3 Fuzzy Inference Engine
102(1)
5.3.4 Defuzzification
102(1)
5.4 Predicting Heart Disease Using Genetic Algorithms
103(2)
5.5 Swarm Intelligence Based Optimization Problem For Heart Disease Diagnosis
105(3)
5.5.1 Ant Colony Optimization
105(1)
5.5.2 Particle Swarm Optimization
106(2)
5.6 Heart Disease Prediction Using Data Mining Techniques
108(2)
5.7 Performance Metrics
110(3)
5.8 Conclusion
113(4)
Chapter 6 Swarm Intelligence and Evolutionary Algorithms for Drug Design and Development
117(24)
Bandana Mahapatra
6.1 Introduction
117(2)
6.2 Drug Design And Development: Past, Present And Future
119(4)
6.3 Role Of Swarm Intelligence In Drug Design And Development
123(3)
6.4 Role Of Evolutionary Algorithms In Drug Design And Development
126(2)
6.5 Qsar Modelling Using Swarm Intelligence And Evolutionary Algorithms
128(3)
6.6 Prediction Of Molecule Activity Swarm Intelligence And Evolutionary Algorithms
131(5)
6.6.1 Particle Swarm Optimization
135(1)
6.7 Conclusion
136(5)
Index 141
Sandeep Kumar, Anand Nayyar, Anand Paul
Biežāk uzdotie jautājumi par e-grāmatām Biežāk uzdotie jautājumi par e-grāmatām
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