Big Data Analytics and Intelligence: A Perspective for Health Care
- Length: 392 pages
- Edition: 1
- Language: English
- Publisher: Emerald Publishing
- Publication Date: 2020-09-30
- ISBN-10: 1839091002
- ISBN-13: 9781839091001
- Sales Rank: #0 (See Top 100 Books)
Big data is a field of research that is growing rapidly, and as the Covid-19 crisis has shown, health care is an area that could benefit greatly from its increased use and application. Big data, as derived partly from the internet of things and analysed according to specific algorithms, has a large and beneficial role to play in preventative medicine, in monitoring the health of specific groups, and in improving diagnostics.
Big Data Analytics and Intelligence: A Perspective for Health Care focuses on various areas of health care, ranging from nutrition to cancer, and providing diverse perspectives on all of them. This book explores the entire life-cycle of big data, from information retrieval to analysis, and it shows how big data’s applications can enhance, streamline and improve services for patients and health-care professionals. Each chapter focuses on a specific area of health care and how big data is applicable to it, with background and current examples provided.
Cover Title Copyright Contents About the Editors About the Authors Preface Chapter 1. Big Data Analytics and Intelligence: A Perspective For Health Care Abstract 1. Introduction 2. Big Data Overview 3. Big Data Applications in Health Care 3.1. Various Sources of Data, Methods, and the Challenges Faced 3.2. Electronic Health Records. Needs and Advantages 3.3. Enhancing Patient Engagement 3.4. Big Data to Understand Cure for Cancer 3.5. Predictive Analytics in Health Care 3.6. Need for Security and a Mechanism to Reduce Fraud in Big Data 3.7. Telemedicine 3.8. Applications of Big Data References Chapter 2. Big Data Analytics in Health Sector: Need, Opportunities, Challenges, and Future Prospects Abstract Introduction Big Data BD Definitions in the Health Sector BD Needs in the Health Sector The Health Care Analytics Environment EHRs EMRs Sensor Data Internet of Things BDA Techniques, Tools, and Technologies in Health Sector Opportunities in Health through BDA Use Challenges and Strategies Few Strategies to Overcome the Challenges of BDA in the Health Sector Conclusion and Prospects References Chapter 3. Use of Classification Algorithms in Health Care Abstract Introduction Data Mining in Health care Classification Algorithms used in the Healthcare Industry k-Nearest Neighbours Naïve Bayes Algorithm Support Vector Machines Decision Trees Chi-square Pruning Random Forest Logistic Regression Sigmoid Function Concept of Cost Function Neural Networks Activation Functions Linear Activation Function Sigmoid Activation Function Hyperbolic Tangent Activation Function Rectified Linear Unit Activation Function Leaky Rectified Linear Unit Activation Function Softmax Activation Function Gradient Descent CNN and RNN Ensemble Learning Boosting Bagging Combination Methods Model Evaluation Receiver Operating Characteristics Curve E-health References Chapter 4. Big Data Analytics in Excelling Health Care: Achievement and Challenges in India Abstract Introduction Importance of Data in Modern Society: Data as Fuel of Modern Economy Concept and Emergence of Big Data Big Data Analytics: Fundamental Concepts Objectives Research Methodology Analysis I Analysis II Definition of Big Data Analytics Historical Perspective of Big Data Analytics Typology of Big Data Analytics Analysis III Applications of Big Data Analytics in Health Sector A. R&D in Heath Sector (Descriptive Analysis: It Would Be Manifested Largely by Descriptive Analytics) B. Treatment Protocol Perceptive Analytics C. Advance Awareness Campaigns Based on Predictive Analysis Achievements Challenges Conclusion References Chapter 5. Predictive Big Data Analytics in Healthcare Abstract 1. Introduction 2. Sources of Big Data in Healthcare 2.1. Clinical Data 2.2. Claims Data 2.3. Clinical Research Data 2.4. Patient – Generated Data 3. Areas of Application of Predictive Big Data Analytics in Healthcare 4. Advantages of Predictive Big Data Analytics in Healthcare 4.1. IT Infrastructure Benefits 4.2. Operational Benefits 4.3. Organizational Benefits 4.4. Managerial Benefits 4.5. Strategic Benefits 5. Challenges of Predictive Big Data Analytics in Healthcare 5.1. Infrastructural Concerns 5.2. Challenges Arising Due to the Technique 5.3. Data-related Concerns 5.4. Security/Privacy Concerns 5.5. Organization-related Concerns 6. Conclusion and Future Scope References Chapter 6. Smart Nursery with Health Monitoring System Through Integration of IoT and Machine Learning Abstract 1. Introduction 2. Literature Survey 2.1. LDR Sensor 2.2. Moisture Sensor 2.3. pH Sensor 2.4. Air Quality 2.5. Conductivity Sensor 2.6. Temperature Sensor 2.7. Pressure Sensor (Barometric Pressure) 2.8. Pressure Sensor (Soil Pressure) 3. Methodology 3.1. Data Acquiring and Preprocessing 3.2. Data Modeling 4. Proposed Solution 5. Results 6. Conclusion 7. Future Scope References Chapter 7. Computer-aided Big Healthcare Data (BHD) Analytics Abstract 1. Introduction 1.2. Methods and Technology Progress in Big Data 2. Motivation 3. Anatomy of Big Data 4. Benefits of BHD Analytics 5. BHD Applications in Real Clinics 5.1. Big Health Data Applications 6. Conclusions References Chapter 8. Intrusion Detection and Security System Abstract 1. Introduction 2. Literature Review 3. System Architecture 3.1. ATMEGA328P 3.2. Infrared Sensor 3.3. MQ-2 Sensor 3.4. LDR Module 3.5. DHT11 3.6. Relay 4. Hardware Assembly and Implementation 4.1. ATmega328P 4.2. Infrared Sensor 4.3. MQ-2 Module 4.4. LDR Module 4.5. DHT11 Module 5. Working 6. Future Scope 6. Conclusion References Chapter 9. Decision Making with BI in Healthcare Domain Abstract 1. Introduction 2. Vision 3. Main Contribution 4. Business Intelligence 4.1 Evolution of BI 4.2 BI in Healthcare Industry and its Benefits 5. Literature Survey 6. Problem Identified 7. Proposed Solution 7.1 Proposed Workflow 7.2 Data Warehouse Design 7.3 Dataset Used 7.4 Extraction, Transformation, and Loading Process 7.5 Implementation 8. Conclusion References Chapter 10. Assistance for Facial Palsy using Quantitative Technology Abstract 1. Introduction 1.1. General Overview 1.2. Background 2. Literature Survey 3. Problem Identified 4. Comparative Study of Already Existing Solution 5. Proposed Solution 6. Pros and Cons of Solution 7. Conclusion 8. Future Scope References Chapter 11. Constructive Effect of Ranking Optimal Features Using Random Forest, SupportVector Machine and Naïve Bayes forBreast Cancer Diagnosis Abstract 1. Introduction 2. Related Work on Breast Cancer Prediction 3. Machine Learning Classifiers 3.1. Random Forest 3.2. Support Vector Machine 3.3. Naïve Bayes 4. Statistical Analysis 5. Proposed Methodology 6. Experimental Results and Discussion 7. Conclusion References Chapter 12. Intelligent Establishment of Correlation of TTH and Diabetes Mellitus on Subject’s Physical Characteristics: MMBD and ML Perspective in Healthcare Abstract Introduction Diabetes Mellitus Stress Migraine Tension-type Headaches (TTH) Obesity Coronary Artery Disease (CAD) The Cases When to See a Doctor AI in Healthcare Machine Learning ML in Healthcare Big Data (BD) Applications of BD in Healthcare Internet of Things (IoT) IoT in Healthcare Role of Technology in Addressing the Problem of Integration of Healthcare System Literature Survey/Previous Findings Our Experiment Results, Interpretation and Discussion Experimental Setup About the Study and Analysis Results and Discussion Age Group and Gender Distribution Diabetes and Insulin Consumption A Novelty in Our Work Future Scope, Possible Applications, and Limitations Recommendations and Consideration Conclusions Acknowledgment References Chapter 13. A Machine Learning Approach Toward Meal Classification and Assessment of Nutrients Value Based on Weather Conditions Abstract Introduction Role and Value of Nutrients in Food Life-threatening Diseases Caused by Unhealthy Food Effect of Weather on Food Summer Eats Winter Eats Spring Eats Autumn Eats Food Security Problem Identified Malnutrition Carbohydrates Deficiency of Carbohydrates Deficiency of Fats Deficiency of Protein Deficiency of Vitamin Deficiency of Minerals Machine Learning Advantages and Disadvantages of SVMs Deep Learning Proposed Solution Future Scope Conclusions References Chapter 14. Telehealth: Former, Today, and Later Abstract Introduction Process of Evolution History of Medicine Pre-Historic ERA Early Civilization Modern History Evolution of Telehealth What is Telehealth? What is Telemedicine? Business Models of Telehealth Optimized Delivery in Telehealth Care Barriers to Telehealth Literature Review Methodology Results References Chapter 15. Predictive Modeling in Health Care Data Analytics: A Sustainable Supervised Learning Technique Abstract 1. Introduction 2. Predictive Analytics in Health Care 3. Techniques for Predictive Modeling 3.1. Artificial Neural Networks 3.2. k-Nearest Neighbors 3.3. Naïve Bayes Classification Modeling 3.4. Decision Trees 3.5. Linear Regression 3.6. Logistic Regression 4. Applications of Predictive Modeling in Health Care 4.1. Disease Diagnosis and Treatment Selection 4.2. Health Care Management 4.3. Reducing Health Care Costs 5. Conclusions References Index
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