When considering the idea of using machine learning in healthcare, it is a Herculean task to present the entire gamut of information in the field of intelligent systems. It is, therefore the objective of this book to keep the presentation narrow and intensive. This approach is distinct from others in that it presents detailed computer simulations for all models presented with explanations of the program code. It includes unique and distinctive chapters on disease diagnosis, telemedicine, medical imaging, smart health monitoring, social media healthcare, and machine learning for COVID-19. These…mehr
When considering the idea of using machine learning in healthcare, it is a Herculean task to present the entire gamut of information in the field of intelligent systems. It is, therefore the objective of this book to keep the presentation narrow and intensive. This approach is distinct from others in that it presents detailed computer simulations for all models presented with explanations of the program code. It includes unique and distinctive chapters on disease diagnosis, telemedicine, medical imaging, smart health monitoring, social media healthcare, and machine learning for COVID-19. These chapters help develop a clear understanding of the working of an algorithm while strengthening logical thinking. In this environment, answering a single question may require accessing several data sources and calling on sophisticated analysis tools. While data integration is a dynamic research area in the database community, the specific needs of research have led to the development of numerous middleware systems that provide seamless data access in a result-driven environment.
Since this book is intended to be useful to a wide audience, students, researchers and scientists from both academia and industry may all benefit from this material. It contains a comprehensive description of issues for healthcare data management and an overview of existing systems, making it appropriate for introductory and instructional purposes. Prerequisites are minimal; the readers are expected to have basic knowledge of machine learning.
This book is divided into 22 real-time innovative chapters which provide a variety of application examples in different domains. These chapters illustrate why traditional approaches often fail to meet customers' needs. The presented approaches provide a comprehensive overview of current technology. Each of these chapters, which are written by the main inventors of the presented systems, specifies requirements and provides a description of both the chosen approach and its implementation. Because of the self-contained nature of these chapters, they may be read in any order. Each of the chapters use various technical terms which involve expertise in machine learning and computer science.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Sachi Nandan Mohanty received his PhD from IIT Kharagpur in 2015. He has recently joined as an associate professor in the Department of Computer Science & Engineering at ICFAI Foundation for Higher Education Hyderabad. His research areas include data mining, big data analysis, cognitive science, fuzzy decision making, brain-computer interface, and computational intelligence. He has published 20 SCI journal articles and has authored/edited 7 books. G. Nalinipriya is a professor in the Department of Information Technology, Anna University, Chennai where she also obtained her PhD. She has more than 23 years of experience in the field of teaching, industry and research and her interests include artificial intelligence, machine learning, data science and cloud security. Om Prakash Jena is an assistant professor in the Department of Computer Science, Ravenshaw University, Cuttack, Odisha. He has 10 years of teaching and research experience and has published several technical papers in international journals/conferences/edited books. His current research interests include pattern recognition, cryptography, network security, soft computing, data analytics and machine automation. Achyuth Sarkar received his PhD in Computer Science and Engineering from the National Institute of Technology, Arunachal Pradesh in 2019. He has teaching experience of more than 10 years.
Inhaltsangabe
Preface xvii
Part 1: Introduction to Intelligent Healthcare Systems 1
1 Innovation on Machine Learning in Healthcare Services--An Introduction 3 Parthasarathi Pattnayak and Om Prakash Jena
1.1 Introduction 3
1.2 Need for Change in Healthcare 5
1.3 Opportunities of Machine Learning in Healthcare 6
1.4 Healthcare Fraud 7
1.4.1 Sorts of Fraud in Healthcare 7
1.4.2 Clinical Service Providers 8
1.4.3 Clinical Resource Providers 8
1.4.4 Protection Policy Holders 8
1.4.5 Protection Policy Providers 9
1.5 Fraud Detection and Data Mining in Healthcare 9
1.5.1 Data Mining Supervised Methods 10
1.5.2 Data Mining Unsupervised Methods 10
1.6 Common Machine Learning Applications in Healthcare 10
1.6.1 Multimodal Machine Learning for Data Fusion in Medical Imaging 11
1.6.2 Machine Learning in Patient Risk Stratification 11
1.6.3 Machine Learning in Telemedicine 11
1.6.4 AI (ML) Application in Sedate Revelation 12
1.6.5 Neuroscience and Image Computing 12
1.6.6 Cloud Figuring Systems in Building AI-Based Healthcare 12
1.6.7 Applying Internet of Things and Machine-Learning for Personalized Healthcare 12
1.6.8 Machine Learning in Outbreak Prediction 13
1.7 Conclusion 13
References 14
Part 2: Machine Learning/Deep Learning-Based Model Development 17
2 A Framework for Health Status Estimation Based on Daily Life Activities Data Using Machine Learning Techniques 19 Tene Ramakrishnudu, T. Sai Prasen and V. Tharun Chakravarthy
2.1 Introduction 19
2.1.1 Health Status of an Individual 19
2.1.2 Activities and Measures of an Individual 20
2.1.3 Traditional Approach to Predict Health Status 20
2.2 Background 20
2.3 Problem Statement 21
2.4 Proposed Architecture 22
2.4.1 Pre-Processing 22
2.4.2 Phase-I 23
2.4.3 Phase-II 23
2.4.4 Dataset Generation 23
2.4.4.1 Rules Collection 23
2.4.4.2 Feature Selection 24
2.4.4.3 Feature Reduction 24
2.4.4.4 Dataset Generation From Rules 24
2.4.4.5 Example 24
2.4.5 Pre-Processing 26
2.5 Experimental Results 27
2.5.1 Performance Metrics 27
2.5.1.1 Accuracy 27
2.5.1.2 Precision 28
2.5.1.3 Recall 28
2.5.1.4 F1-Score 30
2.6 Conclusion 31
References 31
3 Study of Neuromarketing With EEG Signals and Machine Learning Techniques 33 S. Pal, P. Das, R. Sahu and S.R. Dash
3.1 Introduction 34
3.1.1 Why BCI 34
3.1.2 Human-Computer Interfaces 34
3.1.3 What is EEG 35
3.1.4 History of EEG 35
3.1.5 About Neuromarketing 35
3.1.6 About Machine Learning 36
3.2 Literature Survey 36
3.3 Methodology 45
3.3.1 Bagging Decision Tree Classifier 45
3.3.2 Gaussian Naïve Bayes Classifier 45
3.3.3 Kernel Support Vector Machine (Sigmoid) 45
3.3.4 Random Decision Forest Classifier 46
3.4 System Setup & Design 46
3.4.1 Pre-Processing & Feature Extraction 47
3.4.1.1 Savitzky-Golay Filter 47
3.4.1.2 Discrete Wavelet Transform 48
3.4.2 Dataset Description 49
3.5 Result 49
3.5.1 Individual Result Analysis 49
3.5.2 Comparative Results Analysis 52
3.6 Conclusion 53
References 54
4 An Expert System-Based Clinical Decision Support System for Hepatitis-B Prediction & Diagn
Part 1: Introduction to Intelligent Healthcare Systems 1
1 Innovation on Machine Learning in Healthcare Services--An Introduction 3 Parthasarathi Pattnayak and Om Prakash Jena
1.1 Introduction 3
1.2 Need for Change in Healthcare 5
1.3 Opportunities of Machine Learning in Healthcare 6
1.4 Healthcare Fraud 7
1.4.1 Sorts of Fraud in Healthcare 7
1.4.2 Clinical Service Providers 8
1.4.3 Clinical Resource Providers 8
1.4.4 Protection Policy Holders 8
1.4.5 Protection Policy Providers 9
1.5 Fraud Detection and Data Mining in Healthcare 9
1.5.1 Data Mining Supervised Methods 10
1.5.2 Data Mining Unsupervised Methods 10
1.6 Common Machine Learning Applications in Healthcare 10
1.6.1 Multimodal Machine Learning for Data Fusion in Medical Imaging 11
1.6.2 Machine Learning in Patient Risk Stratification 11
1.6.3 Machine Learning in Telemedicine 11
1.6.4 AI (ML) Application in Sedate Revelation 12
1.6.5 Neuroscience and Image Computing 12
1.6.6 Cloud Figuring Systems in Building AI-Based Healthcare 12
1.6.7 Applying Internet of Things and Machine-Learning for Personalized Healthcare 12
1.6.8 Machine Learning in Outbreak Prediction 13
1.7 Conclusion 13
References 14
Part 2: Machine Learning/Deep Learning-Based Model Development 17
2 A Framework for Health Status Estimation Based on Daily Life Activities Data Using Machine Learning Techniques 19 Tene Ramakrishnudu, T. Sai Prasen and V. Tharun Chakravarthy
2.1 Introduction 19
2.1.1 Health Status of an Individual 19
2.1.2 Activities and Measures of an Individual 20
2.1.3 Traditional Approach to Predict Health Status 20
2.2 Background 20
2.3 Problem Statement 21
2.4 Proposed Architecture 22
2.4.1 Pre-Processing 22
2.4.2 Phase-I 23
2.4.3 Phase-II 23
2.4.4 Dataset Generation 23
2.4.4.1 Rules Collection 23
2.4.4.2 Feature Selection 24
2.4.4.3 Feature Reduction 24
2.4.4.4 Dataset Generation From Rules 24
2.4.4.5 Example 24
2.4.5 Pre-Processing 26
2.5 Experimental Results 27
2.5.1 Performance Metrics 27
2.5.1.1 Accuracy 27
2.5.1.2 Precision 28
2.5.1.3 Recall 28
2.5.1.4 F1-Score 30
2.6 Conclusion 31
References 31
3 Study of Neuromarketing With EEG Signals and Machine Learning Techniques 33 S. Pal, P. Das, R. Sahu and S.R. Dash
3.1 Introduction 34
3.1.1 Why BCI 34
3.1.2 Human-Computer Interfaces 34
3.1.3 What is EEG 35
3.1.4 History of EEG 35
3.1.5 About Neuromarketing 35
3.1.6 About Machine Learning 36
3.2 Literature Survey 36
3.3 Methodology 45
3.3.1 Bagging Decision Tree Classifier 45
3.3.2 Gaussian Naïve Bayes Classifier 45
3.3.3 Kernel Support Vector Machine (Sigmoid) 45
3.3.4 Random Decision Forest Classifier 46
3.4 System Setup & Design 46
3.4.1 Pre-Processing & Feature Extraction 47
3.4.1.1 Savitzky-Golay Filter 47
3.4.1.2 Discrete Wavelet Transform 48
3.4.2 Dataset Description 49
3.5 Result 49
3.5.1 Individual Result Analysis 49
3.5.2 Comparative Results Analysis 52
3.6 Conclusion 53
References 54
4 An Expert System-Based Clinical Decision Support System for Hepatitis-B Prediction & Diagn
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