Sandeep Kumar, Anand Nayyar, Anand Paul

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• CONTENTSPreface, xiAbout the Editors, xvContributors, xixAbbreviations, xxiCHAPTER 1 ■ Swarm Intelligence and EvolutionaryAlgorithms in Disease Diagnosis—IntroductoryAspects 1BHUSHAN INJE, SANDEEP KUMAR, AND ANAND NAYYAR1.1 INTRODUCTION 11.2 TERMINOLOGIES 21.2.1 Swarm Intelligence 21.2.1.1 Merits of Swarm Intelligence 31.2.1.2 Classifications and Terminology 41.2.2 Evolutionary Computation 51.2.3 Evolutionary Computation Paradigms 61.3 IMPORTANCE OF SWARM INTELLIGENCE INDISEASE DIAGNOSIS 71.4 IMPORTANCE OF EVOLUTIONARY ALGORITHMSIN DISEASE DIAGNOSIS 101.5 CONCLUSION 14CHAPTER 2 ■ Swarm Intelligence and EvolutionaryAlgorithms for Cancer Diagnosis 19BANDANA MAHAPATRA AND ANAND NAYYAR2.1 INTRODUCTION 192.2 CLASSIFICATION OF CANCER 212.3 CHALLENGES IN CANCER DIAGNOSIS 262.3.1 Methods of Cancer Detection 262.3.2 Issues and Challenges Faced While CancerDetection Process 272.4 APPLYING SWARM INTELLIGENCE ALGORITHMFOR CANCER DIAGNOSIS 282.4.1 SI Algorithms for Detection of Lung Cancer 292.4.2 Swarm Intelligence for Breast Cancer 302.4.3 Swarm Intelligence for Ovarian Cancer 302.4.4 SI Algorithm for Early Detection of Gastro Cancer 302.4.5 Swarm Intelligence for Treating Nano-Robots 312.5 APPLYING EVOLUTIONARY ALGORITHM FORCANCER DETECTION 342.6 CONCLUSION 40CHAPTER 3 ■ Brain Tumour Diagnosis 45DHANANJAY JOSHI, NITIN CHOUBEY, AND RAJANI KUMARI3.1 INTRODUCTION 453.2 APPLYING EVOLUTIONARY ALGORITHMS FORBRAIN TUMOR DIAGNOSIS 503.2.1 Evolutionary Algorithm 503.2.2 Conceptual Framework 1: Applying EvolutionaryAlgorithm for Brain Tumor Diagnosis. 523.3 APPLYING SWARM INTELLIGENCE ALGORITHMSFOR BRAIN TUMOR DIAGNOSIS 543.3.1 Swarm Intelligence (SI) - Based Algorithms 543.3.2 Self-Organization: 553.3.3 Division of Labor: 553.3.4 Particle Swarm Optimization 553.3.5 Particle Swarm Optimization Algorithm 563.3.6 Conceptual Framework 2: Applying SwarmIntelligence Based Algorithm for Brain TumorDiagnosis 573.4 APPLYING SWARM INTELLIGENCE ANDEVOLUTIONARY ALGORITHMS TOGETHER FORDIAGNOSIS OF BRAIN TUMOR 583.5 APPLYING SWARM INTELLIGENCE, EVOLUTIONARYALGORITHM AND INCORPORATING TOPOLOGICALDATA ANALYSIS (TDA) FOR BRAIN TUMORDIAGNOSIS 593.5.1 Topological Data Analysis 593.6 CONCLUSION 59CHAPTER 4 ■ Swarm Intelligence and EvolutionaryAlgorithms for Diabetic RetinopathyDetection 65SACHIN BHANDARI, RADHAKRISHNA RAMBOLA, AND RAJANI KUMARI4.1 INTRODUCTION 654.1.1 Classification of Diabetic Retinopathy 664.1.2 Swarm Optimization and EvolutionaryAlgorithms 694.1.3 Objectives and Contributions 714.2 FEATURE OF DIABETIC RETINOPATHY 724.2.1 Microaneurysms 724.2.2 Haemorrhages 734.2.3 Hard Exudates 734.2.4 Soft Exudates 734.2.5 Neo-Vascularization 744.2.6 Macular Edema 744.3 DETECTION OF DIABETIC RETINOPATHY BYAPPLYING SWARM INTELLIGENCE ANDEVOLUTIONARY ALGORITHMS 744.3.1 Genetic Algorithm 754.3.2 Particle Swarm Optimization 794.3.3 Ant Colony Optimization 814.3.4 Cuckoo Search 844.3.5 Bee Colony Optimization 854.4 CONCLUSION 87CHAPTER 5 ■ Swarm Intelligence and EvolutionaryAlgorithms for Heart Disease Diagnosis 93RAJALAKSHMI KRISHNAMURTHI5.1 INTRODUCTION 935.2 PREDICTION AND CLASSIFICATION OF HEARTDISEASE USING MACHINE LEARNING/SWARMINTELLIGENCE 955.2.1 Decision Support System 955.2.2 Clinical Decision Support System 965.2.3 Heart Disease Datasets 975.3 PREDICTING HEART ATTACKS IN PATIENTSUSING ARTIFICIAL INTELLIGENCE METHODS(FUZZY LOGIC) 985.3.1 Fuzzy Logic Approach for Heart Disease Diagnosis 995.3.2 Fuzzy Rule Base 1015.3.3 Fuzzy Inference Engine 1025.3.4 Defuzzification 1025.4 PREDICTING HEART DISEASE USING GENETICALGORITHMS 1035.5 SWARM INTELLIGENCE BASED OPTIMIZATIONPROBLEM FOR HEART DISEASE DIAGNOSIS 1055.5.1 Ant Colony Optimization 1055.5.2 Particle Swarm Optimization 1065.6 HEART DISEASE PREDICTION USING DATA MININGTECHNIQUES 1085.7 PERFORMANCE METRICS 1105.8 CONCLUSION 113CHAPTER 6 ■ Swarm Intelligence and EvolutionaryAlgorithms for Drug Design andDevelopment 117BANDANA MAHAPATRA6.1 INTRODUCTION 1176.2 DRUG DESIGN AND DEVELOPMENT: PAST, PRESENTAND FUTURE 1196.3 ROLE OF SWARM INTELLIGENCE IN DRUG DESIGNAND DEVELOPMENT 1236.4 ROLE OF EVOLUTIONARY ALGORITHMS IN DRUGDESIGN AND DEVELOPMENT 1266.5 QSAR MODELLING USING SWARM INTELLIGENCEAND EVOLUTIONARY ALGORITHMS 1286.6 PREDICTION OF MOLECULE ACTIVITY SWARMINTELLIGENCE AND EVOLUTIONARY ALGORITHMS 1316.6.1 Particle Swarm Optimization 1356.7 CONCLUSION 136INDEX, 141

Sandeep Kumar, Anand Nayyar, Anand Paul

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• CONTENTSPreface, xiAbout the Editors, xvContributors, xixAbbreviations, xxiCHAPTER 1 ■ Swarm Intelligence and EvolutionaryAlgorithms in Disease Diagnosis—IntroductoryAspects 1BHUSHAN INJE, SANDEEP KUMAR, AND ANAND NAYYAR1.1 INTRODUCTION 11.2 TERMINOLOGIES 21.2.1 Swarm Intelligence 21.2.1.1 Merits of Swarm Intelligence 31.2.1.2 Classifications and Terminology 41.2.2 Evolutionary Computation 51.2.3 Evolutionary Computation Paradigms 61.3 IMPORTANCE OF SWARM INTELLIGENCE INDISEASE DIAGNOSIS 71.4 IMPORTANCE OF EVOLUTIONARY ALGORITHMSIN DISEASE DIAGNOSIS 101.5 CONCLUSION 14CHAPTER 2 ■ Swarm Intelligence and EvolutionaryAlgorithms for Cancer Diagnosis 19BANDANA MAHAPATRA AND ANAND NAYYAR2.1 INTRODUCTION 192.2 CLASSIFICATION OF CANCER 212.3 CHALLENGES IN CANCER DIAGNOSIS 262.3.1 Methods of Cancer Detection 262.3.2 Issues and Challenges Faced While CancerDetection Process 272.4 APPLYING SWARM INTELLIGENCE ALGORITHMFOR CANCER DIAGNOSIS 282.4.1 SI Algorithms for Detection of Lung Cancer 292.4.2 Swarm Intelligence for Breast Cancer 302.4.3 Swarm Intelligence for Ovarian Cancer 302.4.4 SI Algorithm for Early Detection of Gastro Cancer 302.4.5 Swarm Intelligence for Treating Nano-Robots 312.5 APPLYING EVOLUTIONARY ALGORITHM FORCANCER DETECTION 342.6 CONCLUSION 40CHAPTER 3 ■ Brain Tumour Diagnosis 45DHANANJAY JOSHI, NITIN CHOUBEY, AND RAJANI KUMARI3.1 INTRODUCTION 453.2 APPLYING EVOLUTIONARY ALGORITHMS FORBRAIN TUMOR DIAGNOSIS 503.2.1 Evolutionary Algorithm 503.2.2 Conceptual Framework 1: Applying EvolutionaryAlgorithm for Brain Tumor Diagnosis. 523.3 APPLYING SWARM INTELLIGENCE ALGORITHMSFOR BRAIN TUMOR DIAGNOSIS 543.3.1 Swarm Intelligence (SI) - Based Algorithms 543.3.2 Self-Organization: 553.3.3 Division of Labor: 553.3.4 Particle Swarm Optimization 553.3.5 Particle Swarm Optimization Algorithm 563.3.6 Conceptual Framework 2: Applying SwarmIntelligence Based Algorithm for Brain TumorDiagnosis 573.4 APPLYING SWARM INTELLIGENCE ANDEVOLUTIONARY ALGORITHMS TOGETHER FORDIAGNOSIS OF BRAIN TUMOR 583.5 APPLYING SWARM INTELLIGENCE, EVOLUTIONARYALGORITHM AND INCORPORATING TOPOLOGICALDATA ANALYSIS (TDA) FOR BRAIN TUMORDIAGNOSIS 593.5.1 Topological Data Analysis 593.6 CONCLUSION 59CHAPTER 4 ■ Swarm Intelligence and EvolutionaryAlgorithms for Diabetic RetinopathyDetection 65SACHIN BHANDARI, RADHAKRISHNA RAMBOLA, AND RAJANI KUMARI4.1 INTRODUCTION 654.1.1 Classification of Diabetic Retinopathy 664.1.2 Swarm Optimization and EvolutionaryAlgorithms 694.1.3 Objectives and Contributions 714.2 FEATURE OF DIABETIC RETINOPATHY 724.2.1 Microaneurysms 724.2.2 Haemorrhages 734.2.3 Hard Exudates 734.2.4 Soft Exudates 734.2.5 Neo-Vascularization 744.2.6 Macular Edema 744.3 DETECTION OF DIABETIC RETINOPATHY BYAPPLYING SWARM INTELLIGENCE ANDEVOLUTIONARY ALGORITHMS 744.3.1 Genetic Algorithm 754.3.2 Particle Swarm Optimization 794.3.3 Ant Colony Optimization 814.3.4 Cuckoo Search 844.3.5 Bee Colony Optimization 854.4 CONCLUSION 87CHAPTER 5 ■ Swarm Intelligence and EvolutionaryAlgorithms for Heart Disease Diagnosis 93RAJALAKSHMI KRISHNAMURTHI5.1 INTRODUCTION 935.2 PREDICTION AND CLASSIFICATION OF HEARTDISEASE USING MACHINE LEARNING/SWARMINTELLIGENCE 955.2.1 Decision Support System 955.2.2 Clinical Decision Support System 965.2.3 Heart Disease Datasets 975.3 PREDICTING HEART ATTACKS IN PATIENTSUSING ARTIFICIAL INTELLIGENCE METHODS(FUZZY LOGIC) 985.3.1 Fuzzy Logic Approach for Heart Disease Diagnosis 995.3.2 Fuzzy Rule Base 1015.3.3 Fuzzy Inference Engine 1025.3.4 Defuzzification 1025.4 PREDICTING HEART DISEASE USING GENETICALGORITHMS 1035.5 SWARM INTELLIGENCE BASED OPTIMIZATIONPROBLEM FOR HEART DISEASE DIAGNOSIS 1055.5.1 Ant Colony Optimization 1055.5.2 Particle Swarm Optimization 1065.6 HEART DISEASE PREDICTION USING DATA MININGTECHNIQUES 1085.7 PERFORMANCE METRICS 1105.8 CONCLUSION 113CHAPTER 6 ■ Swarm Intelligence and EvolutionaryAlgorithms for Drug Design andDevelopment 117BANDANA MAHAPATRA6.1 INTRODUCTION 1176.2 DRUG DESIGN AND DEVELOPMENT: PAST, PRESENTAND FUTURE 1196.3 ROLE OF SWARM INTELLIGENCE IN DRUG DESIGNAND DEVELOPMENT 1236.4 ROLE OF EVOLUTIONARY ALGORITHMS IN DRUGDESIGN AND DEVELOPMENT 1266.5 QSAR MODELLING USING SWARM INTELLIGENCEAND EVOLUTIONARY ALGORITHMS 1286.6 PREDICTION OF MOLECULE ACTIVITY SWARMINTELLIGENCE AND EVOLUTIONARY ALGORITHMS 1316.6.1 Particle Swarm Optimization 1356.7 CONCLUSION 136INDEX, 141

Sandeep Kumar, Anand Nayyar, Anand Paul

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• CONTENTSPreface, xiAbout the Editors, xvContributors, xixAbbreviations, xxiCHAPTER 1 ■ Swarm Intelligence and EvolutionaryAlgorithms in Disease Diagnosis—IntroductoryAspects 1BHUSHAN INJE, SANDEEP KUMAR, AND ANAND NAYYAR1.1 INTRODUCTION 11.2 TERMINOLOGIES 21.2.1 Swarm Intelligence 21.2.1.1 Merits of Swarm Intelligence 31.2.1.2 Classifications and Terminology 41.2.2 Evolutionary Computation 51.2.3 Evolutionary Computation Paradigms 61.3 IMPORTANCE OF SWARM INTELLIGENCE INDISEASE DIAGNOSIS 71.4 IMPORTANCE OF EVOLUTIONARY ALGORITHMSIN DISEASE DIAGNOSIS 101.5 CONCLUSION 14CHAPTER 2 ■ Swarm Intelligence and EvolutionaryAlgorithms for Cancer Diagnosis 19BANDANA MAHAPATRA AND ANAND NAYYAR2.1 INTRODUCTION 192.2 CLASSIFICATION OF CANCER 212.3 CHALLENGES IN CANCER DIAGNOSIS 262.3.1 Methods of Cancer Detection 262.3.2 Issues and Challenges Faced While CancerDetection Process 272.4 APPLYING SWARM INTELLIGENCE ALGORITHMFOR CANCER DIAGNOSIS 282.4.1 SI Algorithms for Detection of Lung Cancer 292.4.2 Swarm Intelligence for Breast Cancer 302.4.3 Swarm Intelligence for Ovarian Cancer 302.4.4 SI Algorithm for Early Detection of Gastro Cancer 302.4.5 Swarm Intelligence for Treating Nano-Robots 312.5 APPLYING EVOLUTIONARY ALGORITHM FORCANCER DETECTION 342.6 CONCLUSION 40CHAPTER 3 ■ Brain Tumour Diagnosis 45DHANANJAY JOSHI, NITIN CHOUBEY, AND RAJANI KUMARI3.1 INTRODUCTION 453.2 APPLYING EVOLUTIONARY ALGORITHMS FORBRAIN TUMOR DIAGNOSIS 503.2.1 Evolutionary Algorithm 503.2.2 Conceptual Framework 1: Applying EvolutionaryAlgorithm for Brain Tumor Diagnosis. 523.3 APPLYING SWARM INTELLIGENCE ALGORITHMSFOR BRAIN TUMOR DIAGNOSIS 543.3.1 Swarm Intelligence (SI) - Based Algorithms 543.3.2 Self-Organization: 553.3.3 Division of Labor: 553.3.4 Particle Swarm Optimization 553.3.5 Particle Swarm Optimization Algorithm 563.3.6 Conceptual Framework 2: Applying SwarmIntelligence Based Algorithm for Brain TumorDiagnosis 573.4 APPLYING SWARM INTELLIGENCE ANDEVOLUTIONARY ALGORITHMS TOGETHER FORDIAGNOSIS OF BRAIN TUMOR 583.5 APPLYING SWARM INTELLIGENCE, EVOLUTIONARYALGORITHM AND INCORPORATING TOPOLOGICALDATA ANALYSIS (TDA) FOR BRAIN TUMORDIAGNOSIS 593.5.1 Topological Data Analysis 593.6 CONCLUSION 59CHAPTER 4 ■ Swarm Intelligence and EvolutionaryAlgorithms for Diabetic RetinopathyDetection 65SACHIN BHANDARI, RADHAKRISHNA RAMBOLA, AND RAJANI KUMARI4.1 INTRODUCTION 654.1.1 Classification of Diabetic Retinopathy 664.1.2 Swarm Optimization and EvolutionaryAlgorithms 694.1.3 Objectives and Contributions 714.2 FEATURE OF DIABETIC RETINOPATHY 724.2.1 Microaneurysms 724.2.2 Haemorrhages 734.2.3 Hard Exudates 734.2.4 Soft Exudates 734.2.5 Neo-Vascularization 744.2.6 Macular Edema 744.3 DETECTION OF DIABETIC RETINOPATHY BYAPPLYING SWARM INTELLIGENCE ANDEVOLUTIONARY ALGORITHMS 744.3.1 Genetic Algorithm 754.3.2 Particle Swarm Optimization 794.3.3 Ant Colony Optimization 814.3.4 Cuckoo Search 844.3.5 Bee Colony Optimization 854.4 CONCLUSION 87CHAPTER 5 ■ Swarm Intelligence and EvolutionaryAlgorithms for Heart Disease Diagnosis 93RAJALAKSHMI KRISHNAMURTHI5.1 INTRODUCTION 935.2 PREDICTION AND CLASSIFICATION OF HEARTDISEASE USING MACHINE LEARNING/SWARMINTELLIGENCE 955.2.1 Decision Support System 955.2.2 Clinical Decision Support System 965.2.3 Heart Disease Datasets 975.3 PREDICTING HEART ATTACKS IN PATIENTSUSING ARTIFICIAL INTELLIGENCE METHODS(FUZZY LOGIC) 985.3.1 Fuzzy Logic Approach for Heart Disease Diagnosis 995.3.2 Fuzzy Rule Base 1015.3.3 Fuzzy Inference Engine 1025.3.4 Defuzzification 1025.4 PREDICTING HEART DISEASE USING GENETICALGORITHMS 1035.5 SWARM INTELLIGENCE BASED OPTIMIZATIONPROBLEM FOR HEART DISEASE DIAGNOSIS 1055.5.1 Ant Colony Optimization 1055.5.2 Particle Swarm Optimization 1065.6 HEART DISEASE PREDICTION USING DATA MININGTECHNIQUES 1085.7 PERFORMANCE METRICS 1105.8 CONCLUSION 113CHAPTER 6 ■ Swarm Intelligence and EvolutionaryAlgorithms for Drug Design andDevelopment 117BANDANA MAHAPATRA6.1 INTRODUCTION 1176.2 DRUG DESIGN AND DEVELOPMENT: PAST, PRESENTAND FUTURE 1196.3 ROLE OF SWARM INTELLIGENCE IN DRUG DESIGNAND DEVELOPMENT 1236.4 ROLE OF EVOLUTIONARY ALGORITHMS IN DRUGDESIGN AND DEVELOPMENT 1266.5 QSAR MODELLING USING SWARM INTELLIGENCEAND EVOLUTIONARY ALGORITHMS 1286.6 PREDICTION OF MOLECULE ACTIVITY SWARMINTELLIGENCE AND EVOLUTIONARY ALGORITHMS 1316.6.1 Particle Swarm Optimization 1356.7 CONCLUSION 136INDEX, 141