Preface xiii 1 An Introduction to Big Data Analytics Techniques in Healthcare 1Anil Audumbar Pise 1.1 Introduction 1 1.2 Big Data in Healthcare 3 1.3 Areas of Big Data Analytics in Medicine 5 1.4 Healthcare as a Big Data Repository 9 1.5 Applications of Healthcare Big Data 10 1.6 Challenges in Big Data Analytics 16 1.7 Big Data Privacy and Security 17 1.8 Conclusion 18 1.9 Future Work 18 2 Identify Determinants of Infant and Child Mortality Based Using Machine Learning: Case Study on Ethiopia 21Sudhir Kumar Mohapatra, Srinivas Prasad, Getachew Mekuria Habtemariam and Mohammed Siddique 2.1 Introduction 22 2.2 Literature Review 23 2.3 Methodology and Data Source 25 2.4 Implementation and Results 28 2.5 Conclusion 44 3 Pre-Trained CNN Models in Early Alzheimer's Prediction Using Post-Processed MRI 47Kalyani Gunda and Pradeepini Gera 3.1 Introduction 48 3.2 Experimental Study 51 3.3 Data Exploration 55 3.4 OASIS Dataset Pre-Processing 61 3.5 Alzheimer's 4-Class-MRI Features Extraction 69 3.6 Alzheimer 4-Class MRI Image Dataset 69 3.7 RMSProp (Root Mean Square Propagation) 80 3.8 Activation Function 81 3.9 Batch Normalization 81 3.10 Dropout 81 3.11 Result--I 82 3.12 Conclusion and Future Work 89 4 Robust Segmentation Algorithms for Retinal Blood Vessels, Optic Disc, and Optic Cup of Retinal Images in Medical Imaging 97Birendra Biswal, Raveendra T., Dwiti Krishna Bebarta, Geetha Pavani P. and P.K. Biswal 4.1 Introduction 98 4.2 Basics of Proposed Methods 100 4.3 Experimental Results and Discussion 107 4.4 Conclusion 115 5 Analysis of Healthcare Systems Using Computational Approaches 119Hemanta Kumar Bhuyan and Subhendu Kumar Pani 5.1 Introduction 120 5.2 AI & ML Analysis in Health Systems 124 5.3 Healthcare Intellectual Approaches 127 5.4 Precision Approaches to Medicine 133 5.5 Methodology of AI, ML With Healthcare Examples 134 5.6 Big Analytic Data Tools 136 5.7 Discussion 141 5.8 Conclusion 142 6 Expert Systems in Behavioral and Mental Healthcare: Applications of AI in Decision-Making and Consultancy 147Shrikaant Kulkarni 6.1 Introduction 148 6.2 AI Methods 149 6.3 Turing Test 156 6.4 Barriers to Technologies 157 6.5 Advantages of AI for Behavioral & Mental Healthcare 157 6.6 Enhanced Self-Care & Access to Care 158 6.7 Other Considerations 160 6.8 Expert Systems in Mental & Behavioral Healthcare 161 6.9 Dynamical Approaches to Clinical AI and Expert Systems 165 6.10 Conclusion 173 6.11 Future Prospects 175 7 A Mathematical-Based Epidemic Model to Prevent and Control Outbreak of Corona Virus 2019 (COVID-19) 187Shanmuk Srinivas Amiripalli, Vishnu Vardhan Reddy Kollu, Ritika Prasad and Mukkamala S.N.V. Jitendra 7.1 Introduction 188 7.2 Related Work 189 7.3 Proposed Frameworks 190 7.4 Results and Discussion 194 7.5 Conclusion 201 8 An Access Authorization Mechanism for Electronic Health Records of Blockchain to Sheathe Fragile Information 205Sowjanya Naidu K. and Srinivasa L. Chakravarthy 8.1 Introduction 206 8.2 Related Work 212 8.3 Need for Blockchain in Healthcare 216 8.4 Proposed Frameworks 219 8.5 Use Cases 223 8.6 Discussions 229 8.7 Challenges and Limitations 231 8.8 Future Work 231 8.9 Conclusion 232 9 An Epidemic Graph's Modeling Application to the COVID-19 Outbreak 237Hemanta Kumar Bhuyan and Subhendu Kumar Pani 9.1 Introduction 237 9.2 Related Work 239 9.3 Theoretical Approaches 240 9.4 Frameworks 243 9.5 Evaluation of COVID-19 Outbreak 246 9.6 Conclusions and Future Works 250 10 Big Data and Data Mining in e-Health: Legal Issues and Challenges 257Amita Verma and Arpit Bansal Object of Study 257 10.1 Introduction 258 10.2 Big Data and Data Mining in e-Health 260 10.3 Big Data and e-Health in India 262 10.4 Legal Issues Arising Out of Big Data and Data Mining in e-Health 263 10.5 Big Data and Issues of Privacy in e-Health 271 10.6 Conclusion and Suggestions 272 11 Basic Scientific and Clinical Applications 275Manna Sheela Rani Chetty and Kiran Babu C. V. 11.1 Introduction 275 11.2 Case Study-1: Continual Learning Using ML for Clinical pplications 283 11.3 Case Study-2 286 11.4 Case Study-3: ML Will Improve the Radiology Patient Experience 289 11.5 Case Study-4: Medical Imaging AI with Transition from Academic Research to Commercialization 292 11.6 Case Study-5: ML will Benefit All Medical Imaging 'ologies' 295 11.7 Case Study-6: Health Providers will Leverage Data Hubs to Unlock the Value of Their Data 298 11.8 Conclusion 300 12 Healthcare Branding Through Service Quality 305Saraju Prasad and Sunil Dhal 12.1 Introduction to Healthcare 305 12.2 Quality in Healthcare 307 12.3 Service Quality 311 12.4 Conclusion and Road Ahead 315 References 316 Index 321

Provides coverage of developments and state-of-the-art methods in the broad and diversified data analytics field and applicable areas such as big data analytics, data mining, and machine intelligence in biomedical and health informatics. The novel applications of Big Data Analytics and machine intelligence in the biomedical and healthcare sector is an emerging field comprising computer science, medicine, biology, natural environmental engineering, and pattern recognition. Biomedical and health informatics is a new era that brings tremendous opportunities and challenges due to the plentifully available biomedical data and the aim is to ensure high-quality and efficient healthcare by analyzing the data. The 12 chapters in??Big Data Analytics and Machine Intelligence in Biomedical and Health Informatics??cover the latest advances and developments in health informatics, data mining, machine learning, and artificial intelligence. They have been organized with respect to the similarity of topics addressed, ranging from issues pertaining to the Internet of Things (IoT) for biomedical engineering and health informatics, computational intelligence for medical data processing, and Internet of Medical Things??(IoMT). New researchers and practitioners working in the field will benefit from reading the book as they can quickly ascertain the best performing methods and compare the different approaches. Audience Researchers and practitioners working in the fields of biomedicine, health informatics, big data analytics, Internet of Things, and machine learning.