Evolutionary Intelligence for Healthcare Applications

Description

This book highlights various evolutionary algorithm techniques for various medical conditions and introduces medical applications of evolutionary computation for real-time diagnosis. Evolutionary Intelligence for Healthcare Applications presents how evolutionary intelligence can be used in smart healthcare systems involving big data analytics, mobile health, personalized medicine, and clinical trial data management. It focuses on emerging concepts and approaches and highlights various evolutionary algorithm techniques used for early disease diagnosis, prediction, and prognosis for medical conditions. The book also presents ethical issues and challenges that can occur within the healthcare system. Researchers, healthcare professionals, data scientists, systems engineers, students, programmers, clinicians, and policymakers will find this book of interest. Cover Half Title Series Page Title Page Copyright Page Contents Preface Acknowledgments About the Authors 1. Evolutionary Intelligence 1.1. Introduction 1.2. Preliminaries 1.2.1. Evolutionary Computation 1.3. Evolutionary Algorithms 1.3.1. Representation of EA 1.3.2. Components of Evolutionary Algorithms (EAs) 1.3.3. What Varieties of EA Are There to Choose from? 1.3.4. Typical EA Pseudo Code 1.4. Role of Ea in Healthcare 1.5. Conclusion References 2. Heart Disease Diagnosis 2.1. Introduction 2.2. Heart Attack 2.2.1. Heart Attack 2.2.2. Arrhythmia 2.2.3. Heart Value Complications 2.2.4. Hypertension – Heart Disease 2.3. Heart Disease Classification Using EA 2.3.1. Preprocessing 2.3.2. Feature Selection 2.3.3. Filter Methods 2.3.4. Wrapper Methods 2.3.5. Forward Feature Selection 2.3.6. Backward Feature Elimination 2.3.7. Embedded Methods 2.3.8. LASSO Regularization (L1) 2.3.9. Random Forest Importance 2.4. Challenges and Issues in Heart Disease Diagnosis 2.4.1. Traditional Systems 2.4.2. Existing Methodologies for Diagnosing Heart Diseases 2.5. EA for Heart Disease Diagnosis 2.6. Conclusion References 3. Diabetes Prediction and Classification 3.1. Introduction 3.2. Diabetes Types 3.3. Type 2 Diabetes Mellitus 3.4. Gestational Diabetes 3.5. the Different Diabetes Types 3.5.1. Retinopathy and Associated Disorders 3.5.2. Renal Pathology Nephropathy 3.5.3. Neuropathy 3.6. EA for Diabetes 3.6.1. Hemorrhages 3.6.2. Hard Exudates 3.6.3. Soft-Consistency Effluents 3.7. Genetic Programming 3.8. Blood Vessel Division and Segmentation 3.9. Conclusion References 4. Degenerative Diseases 4.1. Introduction 4.1.1. Neurodegenerative Disease Classification 4.2. Early Prediction of Neurodegenerative Disease and Challenges 4.2.1. Early Prediction – Alzheimer’s Disease 4.2.2. Early Prediction – Parkinson’s Disease 4.3. EA for Treating Degenerative Disorders 4.3.1. Genetic Algorithms in Diagnosing Degenerative Disorders (DD) 4.4. Conclusion References 5. Tuberculosis 5.1. Introduction 5.2. Tuberculosis Classification 5.2.1. Pulmonary 5.2.2. Extrapulmonary 5.2.3. Challenges in Diagnosing PTB and EPTB 5.3. EA for Diagnosing Tuberculosis 5.3.1. Role of EA in Tuberculosis Treatment 5.4. Conclusion References 6. Muscular Dystrophy 6.1. Introduction 6.1.1. Causes of Muscular Dystrophy 6.1.2. Types of Muscular Dystrophy 6.1.3. Diagnosing Muscular Dystrophy 6.1.4. Treating Muscular Dystrophy 6.1.5. Common Muscular Dsytrophy 6.2. Early Clinical Diagnosis of MD 6.3. EA for Diagnosing Muscular Dystrophy 6.4. Conclusion References 7. Tumor Prediction and Classification 7.1. Introduction 7.2. Tumor Types 7.2.1. Carcinogenic 7.2.2. Noncancerous 7.2.3. Precancerous 7.3. Carcinoma Classification 7.3.1. Lung Carcinoma 7.3.2. Blood Carcinoma 7.3.3. Colon Carcinoma 7.3.4. Bone Cancer 7.3.5. Liver Carcinoma 7.3.6. Bladder Carcinoma 7.4. EA for Tumor Classification 7.4.1. Feature Selection 7.4.2. Parameter Optimization 7.5. EA for Carcinoma Prediction 7.5.1. Feature Selection 7.5.2. Parameter Optimization 7.6. Conclusion References Index