Applications of Big Data in Healthcare: Theory and Practice
- Length: 310 pages
- Edition: 1
- Language: English
- Publisher: Academic Press
- Publication Date: 2021-03-26
- ISBN-10: 0128202033
- ISBN-13: 9780128202036
- Sales Rank: #0 (See Top 100 Books)
Applications of Big Data in Healthcare: Theory and Practice begins with the basics of Big Data analysis and introduces the tools, processes and procedures associated with Big Data analytics. The book unites healthcare with Big Data analysis and uses the advantages of the latter to solve the problems faced by the former. The authors present the challenges faced by the healthcare industry, including capturing, storing, searching, sharing and analyzing data. This book illustrates the challenges in the applications of Big Data and suggests ways to overcome them, with a primary emphasis on data repositories, challenges, and concepts for data scientists, engineers and clinicians.
The applications of Big Data have grown tremendously within the past few years and its growth can not only be attributed to its competence to handle large data streams but also to its abilities to find insights from complex, noisy, heterogeneous, longitudinal and voluminous data. The main objectives of Big Data in the healthcare sector is to come up with ways to provide personalized healthcare to patients by taking into account the enormous amounts of already existing data.
Title-page_2021_Applications-of-Big-Data-in-Healthcare Applications of Big Data in Healthcare Copyright_2021_Applications-of-Big-Data-in-Healthcare Copyright Contents_2021_Applications-of-Big-Data-in-Healthcare Contents List-of-contributors_2021_Applications-of-Big-Data-in-Healthcare List of contributors About-the-authors_2021_Applications-of-Big-Data-in-Healthcare About the authors Preface_2021_Applications-of-Big-Data-in-Healthcare Preface Objective of the book Organization of the book 1---Big-Data-classification--techniques-_2021_Applications-of-Big-Data-in-He 1 Big Data classification: techniques and tools 1.1 Introduction 1.2 Big Data classification 1.2.1 Definition of classification 1.2.2 Need for classification in Big Data 1.2.3 Challenges in Big Data classification 1.2.4 Types of classification 1.2.5 Big Data classification approaches 1.2.6 Phases of classification 1.2.7 Classification pattern 1.3 Big Data classification techniques 1.3.1 Traditional learning techniques 1.3.1.1 Logistic regression 1.3.1.2 Support vector machine 1.3.1.3 Decision tree 1.3.1.4 Naïve Bayes algorithm 1.3.1.5 K-nearest neighbor 1.3.1.6 Random forest 1.3.1.7 Matrix factorization 1.3.2 Evolutionary techniques 1.3.2.1 Swarm intelligence 1.3.2.1.1 Particle swarm optimization 1.3.2.1.2 Ant colony optimization 1.3.2.2 Genetic programming 1.3.2.3 Genetic algorithm 1.3.2.4 Artificial neural network 1.3.2.5 Coevolutionary programming 1.3.3 Advanced learning techniques 1.3.3.1 Representation learning 1.3.3.2 Deep learning 1.3.3.3 Distributed and parallel learning 1.3.3.4 Transfer learning 1.3.3.5 Active learning 1.3.3.6 Kernel-based learning 1.4 Big Data classification tools and platforms 1.4.1 Shogun 1.4.2 Scikit-learn 1.4.3 TensorFlow 1.4.4 Pattern 1.4.5 Weka 1.4.6 BigML 1.4.7 DataRobot 1.4.8 Google Cloud AutoML 1.4.9 IBM Watson Studio 1.4.10 MLJAR 1.4.11 Rapidminer 1.4.12 Tableau 1.4.13 Azure Machine Learning Studio 1.4.14 H2O Driverless AI 1.4.15 Apache Mahout 1.4.16 Apache Spark (MLib) 1.4.17 Apache Storm 1.5 Conclusion References 2---Big-Data-Analytics-for-healthcare--theor_2021_Applications-of-Big-Data-i 2 Big Data Analytics for healthcare: theory and applications 2.1 Introduction to Big Data 2.1.1 Motivation 2.2 Big Data Analytics 2.2.1 Techniques and technologies 2.2.2 How Big Data Analytics work 2.2.3 Uses and challenges 2.3 Big Data in healthcare sector 2.4 Medical imaging 2.5 Methodology 2.6 Big Data Analytics: platforms and tools 2.6.1 Cloud storage 2.6.2 NoSQL databases 2.6.3 Hadoop 2.6.4 Hive 2.6.5 Pig 2.6.6 Cassandra 2.7 Opportunities for Big Data in healthcare 2.7.1 Quality of treatment 2.7.2 Early disease detection 2.7.3 Data accessibility and decision-making 2.7.4 Cost reduction 2.8 Challenges to Big Data Analytics in healthcare 2.8.1 Data acquisition and modeling 2.8.2 Data storage and transfer 2.8.3 Data security and risk 2.8.4 Querying and reporting 2.8.5 Technology incorporation and miscommunication gaps 2.9 Applications of Big Data in healthcare industry 2.9.1 Advanced patient monitoring and alerts 2.9.2 Management and operational efficiency 2.9.3 Fraud and error prevention 2.9.4 Enhanced patient engagement 2.9.5 Smart healthcare intelligence 2.10 Future of Big Data in healthcare References 3---Application-of-tools-and-techniques-of-Big-da_2021_Applications-of-Big-D 3 Application of tools and techniques of Big data analytics for healthcare system 3.1 Introduction 3.2 Need and past work 3.2.1 Importance and motivation 3.2.2 Background 3.3 Methods of application 3.3.1 Feature extraction 3.3.2 Imputation 3.4 Result domains 3.4.1 Bioinformatics 3.4.2 Neuroinformatic 3.4.3 Clinical informatics 3.4.4 MRI data for prediction 3.4.5 ICU readmission and mortality rates 3.4.6 Analyzing real-time data streams for diagnosis and prognosis 3.4.7 Public health informatics 3.4.8 Search query data 3.4.9 Social media analytics 3.5 Discussion 3.5.1 Past shortcomings 3.6 Conclusion References 4---Healthcare-and-medical-Big-Data-an_2021_Applications-of-Big-Data-in-Heal 4 Healthcare and medical Big Data analytics 4.1 Introduction 4.2 Medical and healthcare Big Data 4.2.1 Exposome data 4.3 Big Data Analytics 4.3.1 Unsupervised learning 4.3.2 Supervised learning 4.3.3 Semisupervised learning 4.4 Healthcare and medical data coding and taxonomy 4.5 Medical and healthcare data interchange standards 4.6 Framework for healthcare information system based on Big Data 4.7 Big Data security, privacy, and governance 4.8 Discussion and further work References 5---Big-Data-analytics-in-medical-ima_2021_Applications-of-Big-Data-in-Healt 5 Big Data analytics in medical imaging 5.1 Introduction 5.1.1 Medical imaging 5.1.2 Challenges in medical imaging 5.2 Big Data analytics in medical imaging 5.2.1 Analytical methods 5.2.2 Collection, sharing, and compression 5.3 Artificial intelligence for analytics of medical images 5.4 Tools and frameworks 5.4.1 MapReduce 5.4.2 Hadoop 5.4.3 Yet Another Resource Negotiator 5.4.4 Spark 5.5 Conclusion References 6---Big-Data-analytics-and-artificial-intellig_2021_Applications-of-Big-Data 6 Big Data analytics and artificial intelligence in mental healthcare 6.1 Introduction 6.2 What makes mental healthcare complex? 6.3 Opportunities and limitations for artificial intelligence and big data in mental health 6.3.1 Diagnosis 6.3.2 Prognosis 6.3.3 Treatment selection 6.3.4 Treatment delivery 6.3.4.1 Special opportunities 6.3.4.1.1 Real-world validation 6.3.4.1.2 Big Data Loop 6.3.4.2 Specific challenges 6.3.4.2.1 Public acceptance and adoption 6.3.4.2.2 Differentiation 6.3.5 Monitoring 6.3.5.1 Symptom monitoring 6.3.5.2 Monitoring compliance to treatment 6.3.6 Ethical considerations 6.4 Conclusions Acknowledgments References 7---Big-Data-based-breast-cancer-prediction-using-kern_2021_Applications-of- 7 Big Data based breast cancer prediction using kernel support vector machine with the Gray Wolf Optimization algorithm 7.1 Introduction 7.2 Literature survey 7.3 Proposed methodology 7.3.1 Preprocessing 7.3.2 Feature selection 7.3.2.1 Oppositional grasshopper optimization algorithm 7.3.3 Kernel based support vector machine with Gray Wolf Optimization 7.3.4 Dataset description 7.4 Result and discussion 7.4.1 Comparison measures 7.5 Conclusion References 8---Big-Data-based-medical-data-classification-using-_2021_Applications-of-B 8 Big Data based medical data classification using oppositional Gray Wolf Optimization with kernel ridge regression 8.1 Introduction 8.2 Literature survey 8.3 Proposed methodology 8.3.1 Feature reduction 8.3.2 Feature selection 8.3.2.1 Oppositional fruit fly algorithm 8.3.3 Classification using OGWOKRRG 8.3.3.1 Initialization process 8.3.3.2 Fitness evaluation 8.3.3.3 Separate the solution based on the fitness 8.3.3.4 Encircling prey 8.3.3.5 Hunting 8.3.3.6 Attacking prey (exploitation) and Search for prey (exploration) 8.4 Result and discussion 8.4.1 Classification accuracy 8.4.2 Sensitivity 8.4.3 Specificity 8.4.4 Performance evaluation 8.4.5 Comparative analysis 8.5 Conclusion References 9---An-analytical-hierarchical-process-evaluation-on-_2021_Applications-of-B 9 An analytical hierarchical process evaluation on parameters Apps-based Data Analytics for healthcare services 9.1 Introduction 9.2 Review of literature 9.3 Research methodology 9.3.1 Analytic hierarchy processing model 9.3.2 Analytic hierarchy processing technique 9.4 Proposed analytical hierarchy processing model of successful healthcare 9.4.1 Hospital/lab (C2) 9.4.2 Analytic hierarchy processing model description 9.5 Conclusion Appendix Big data analytics for healthcare References 10---Firefly-Binary-Cuckoo-Search-Technique-based-h_2021_Applications-of-Big 10 Firefly—Binary Cuckoo Search Technique based heart disease prediction in Big Data Analytics 10.1 Introduction 10.2 Literature survey 10.3 Proposed methodology 10.3.1 Preprocessing 10.3.2 Optimal feature selection using bacterial foraging optimization 10.3.3 Optimization by using firefly—Binary Cuckoo Search 10.3.3.1 Solution representation 10.3.3.2 Fitness evaluation 10.3.3.3 Firefly updation 10.3.3.4 Initialization phase 10.3.4 Dataset description 10.4 Result and discussion 10.4.1 Comparative analysis 10.5 Conclusion References Further reading 11---Hybrid-technique-for-heart-diseases-diagnosis-ba_2021_Applications-of-B 11 Hybrid technique for heart diseases diagnosis based on convolution neural network and long short-term memory 11.1 Introduction 11.1.1 Heart disease 11.1.2 Traditional ways 11.1.3 The classification techniques 11.2 Literature review 11.3 The proposed technique 11.3.1 Preprocessing data 11.3.2 Building classifier model 11.4 Experimental results and discussion 11.4.1 Evaluation criteria 11.5 Results analysis and discussion 11.5.1 Scenario 11.5.1.1 Dataset 11.5.1.2 Dataset 11.5.1.3 Scenario 11.6 Conclusion References Further reading Index_2021_Applications-of-Big-Data-in-Healthcare Index
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