The book is intended towards graduates and postgraduates information technology and computer science. It will be beneficial for healthcare professionals in the area of biotechnology, general medicine and pharmacy.

Sandeep Kumar, Anand Nayyar, Anand Paul

CONTENTS

Preface, xi

About the Editors, xv

Contributors, xix

Abbreviations, xxi

CHAPTER 1 ¿ Swarm Intelligence and Evolutionary

Algorithms in Disease Diagnosis-Introductory

Aspects 1

BHUSHAN INJE, SANDEEP KUMAR, AND ANAND NAYYAR

1.1 INTRODUCTION 1

1.2 TERMINOLOGIES 2

1.2.1 Swarm Intelligence 2

1.2.1.1 Merits of Swarm Intelligence 3

1.2.1.2 Classifications and Terminology 4

1.2.2 Evolutionary Computation 5

1.2.3 Evolutionary Computation Paradigms 6

1.3 IMPORTANCE OF SWARM INTELLIGENCE IN

DISEASE DIAGNOSIS 7

1.4 IMPORTANCE OF EVOLUTIONARY ALGORITHMS

IN DISEASE DIAGNOSIS 10

1.5 CONCLUSION 14

CHAPTER 2 ¿ Swarm Intelligence and Evolutionary

Algorithms for Cancer Diagnosis 19

BANDANA MAHAPATRA AND ANAND NAYYAR

2.1 INTRODUCTION 19

2.2 CLASSIFICATION OF CANCER 21

2.3 CHALLENGES IN CANCER DIAGNOSIS 26

2.3.1 Methods of Cancer Detection 26

2.3.2 Issues and Challenges Faced While Cancer

Detection Process 27

2.4 APPLYING SWARM INTELLIGENCE ALGORITHM

FOR CANCER DIAGNOSIS 28

2.4.1 SI Algorithms for Detection of Lung Cancer 29

2.4.2 Swarm Intelligence for Breast Cancer 30

2.4.3 Swarm Intelligence for Ovarian Cancer 30

2.4.4 SI Algorithm for Early Detection of Gastro Cancer 30

2.4.5 Swarm Intelligence for Treating Nano-Robots 31

2.5 APPLYING EVOLUTIONARY ALGORITHM FOR

CANCER DETECTION 34

2.6 CONCLUSION 40

CHAPTER 3 ¿ Brain Tumour Diagnosis 45

DHANANJAY JOSHI, NITIN CHOUBEY, AND RAJANI KUMARI

3.1 INTRODUCTION 45

3.2 APPLYING EVOLUTIONARY ALGORITHMS FOR

BRAIN TUMOR DIAGNOSIS 50

3.2.1 Evolutionary Algorithm 50

3.2.2 Conceptual Framework 1: Applying Evolutionary

Algorithm for Brain Tumor Diagnosis. 52

3.3 APPLYING SWARM INTELLIGENCE ALGORITHMS

FOR BRAIN TUMOR DIAGNOSIS 54

3.3.1 Swarm Intelligence (SI) - Based Algorithms 54

3.3.2 Self-Organization: 55

3.3.3 Division of Labor: 55

3.3.4 Particle Swarm Optimization 55

3.3.5 Particle Swarm Optimization Algorithm 56

3.3.6 Conceptual Framework 2: Applying Swarm

Intelligence Based Algorithm for Brain Tumor

Diagnosis 57

3.4 APPLYING SWARM INTELLIGENCE AND

EVOLUTIONARY ALGORITHMS TOGETHER FOR

DIAGNOSIS OF BRAIN TUMOR 58

3.5 APPLYING SWARM INTELLIGENCE, EVOLUTIONARY

ALGORITHM AND INCORPORATING TOPOLOGICAL

DATA ANALYSIS (TDA) FOR BRAIN TUMOR

DIAGNOSIS 59

3.5.1 Topological Data Analysis 59

3.6 CONCLUSION 59

CHAPTER 4 ¿ Swarm Intelligence and Evolutionary

Algorithms for Diabetic Retinopathy

Detection 65

SACHIN BHANDARI, RADHAKRISHNA RAMBOLA, AND RAJANI KUMARI

4.1 INTRODUCTION 65

4.1.1 Classification of Diabetic Retinopathy 66

4.1.2 Swarm Optimization and Evolutionary

Algorithms 69

4.1.3 Objectives and Contributions 71

4.2 FEATURE OF DIABETIC RETINOPATHY 72

4.2.1 Microaneurysms 72

4.2.2 Haemorrhages 73

4.2.3 Hard Exudates 73

4.2.4 Soft Exudates 73

4.2.5 Neo-Vascularization 74

4.2.6 Macular Edema 74

4.3 DETECTION OF DIABETIC RETINOPATHY BY

APPLYING SWARM INTELLIGENCE AND

EVOLUTIONARY ALGORITHMS 74

4.3.1 Genetic Algorithm 75

4.3.2 Particle Swarm Optimization 79

4.3.3 Ant Colony Optimization 81

4.3.4 Cuckoo Search 84

4.3.5 Bee Colony Optimization 85

4.4 CONCLUSION 87

CHAPTER 5 ¿ Swarm Intelligence and Evolutionary

Algorithms for Heart Disease Diagnosis 93

RAJALAKSHMI KRISHNAMURTHI

5.1 INTRODUCTION 93

5.2 PREDICTION AND CLASSIFICATION OF HEART

DISEASE USING MACHINE LEARNING/SWARM

INTELLIGENCE 95

5.2.1 Decision Support System 95

5.2.2 Clinical Decision Support System 96

5.2.3 Heart Disease Datasets 97

5.3 PREDICTING HEART ATTACKS IN PATIENTS

USING ARTIFICIAL INTELLIGENCE METHODS

(FUZZY LOGIC) 98

5.3.1 Fuzzy Logic Approach for Heart Disease Diagnosis 99

5.3.2 Fuzzy Rule Base 101

5.3.3 Fuzzy Inference Engine 102

5.3.4 Defuzzification 102

5.4 PREDICTING HEART DISEASE USING GENETIC

ALGORITHMS 103

5.5 SWARM INTELLIGENCE BASED OPTIMIZATION

PROBLEM FOR HEART DISEASE DIAGNOSIS 105

5.5.1 Ant Colony Optimization 105

5.5.2 Particle Swarm Optimization 106

5.6 HEART DISEASE PREDICTION USING DATA MINING

TECHNIQUES 108

5.7 PERFORMANCE METRICS 110

5.8 CONCLUSION 113

CHAPTER 6 ¿ Swarm Intelligence and Evolutionary

Algorithms for Drug Design and

Development 117

BANDANA MAHAPATRA

6.1 INTRODUCTION 117

6.2 DRUG DESIGN AND DEVELOPMENT: PAST, PRESENT

AND FUTURE 119

6.3 ROLE OF SWARM INTELLIGENCE IN DRUG DESIGN

AND DEVELOPMENT 123

6.4 ROLE OF EVOLUTIONARY ALGORITHMS IN DRUG

DESIGN AND DEVELOPMENT 126

6.5 QSAR MODELLING USING SWARM INTELLIGENCE

AND EVOLUTIONARY ALGORITHMS 128

6.6 PREDICTION OF MOLECULE ACTIVITY SWARM

INTELLIGENCE AND EVOLUTIONARY ALGORITHMS 131

6.6.1 Particle Swarm Optimization 135

6.7 CONCLUSION 136

INDEX, 141