BIOINFORMATICS AND MEDICAL APPLICATIONS The main topics addressed in this book are big data analytics problems in bioinformatics research such as microarray data analysis, sequence analysis, genomics-based analytics, disease network analysis, techniques for big data analytics, and health information technology. Bioinformatics and Medical Applications: Big Data Using Deep Learning Algorithms analyses massive biological datasets using computational approaches and the latest cutting-edge technologies to capture and interpret biological data. The book delivers various bioinformatics…mehr
The main topics addressed in this book are big data analytics problems in bioinformatics research such as microarray data analysis, sequence analysis, genomics-based analytics, disease network analysis, techniques for big data analytics, and health information technology.
Bioinformatics and Medical Applications: Big Data Using Deep Learning Algorithms analyses massive biological datasets using computational approaches and the latest cutting-edge technologies to capture and interpret biological data. The book delivers various bioinformatics computational methods used to identify diseases at an early stage by assembling cutting-edge resources into a single collection designed to enlighten the reader on topics focusing on computer science, mathematics, and biology. In modern biology and medicine, bioinformatics is critical for data management. This book explains the bioinformatician's important tools and examines how they are used to evaluate biological data and advance disease knowledge.
The editors have curated a distinguished group of perceptive and concise chapters that presents the current state of medical treatments and systems and offers emerging solutions for a more personalized approach to healthcare. Applying deep learning techniques for data-driven solutions in health information allows automated analysis whose method can be more advantageous in supporting the problems arising from medical and health-related information.
Audience
The primary audience for the book includes specialists, researchers, postgraduates, designers, experts, and engineers, who are occupied with biometric research and security-related issues.
A. Suresh, PhD is an associate professor, Department of the Networking and Communications, SRM Institute of Science & Technology, Kattankulathur, Tamil Nadu, India. He has nearly two decades of experience in teaching and his areas of specialization are data mining, artificial intelligence, image processing, multimedia, and system software. He has published 6 patents and more than 100 papers in international journals. S. Vimal, PhD is an assistant professor in the Department of Artificial Intelligence & DS, Ramco Institute of Technology, Tamilnadu, India. He is the editor of 3 books and guest-edited multiple journal special issues. He has more than 15 years of teaching experience. Y. Harold Robinson, PhD is currently working in the School of Technology and Engineering, Vellore Institute of Technology, Vellore, India. He has published more than 50 papers in various international journals and presented more than 70 papers in both national and international conferences. Dhinesh Kumar Ramaswami, BE in Computer Science, is a Senior Consultant at Capgemini America Inc. He has over 9 years of experience in software development and specializes in various .net technologies. He has published more than 15 papers in international journals and national and international conferences. R. Udendhran, PhD is an assistant professor, Department of Computer Science and Engineering at Sri Sairam Institute of Technology, Chennai, Tamil Nadu, India. He has published about 20 papers in international journals.
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Preface xv
1 Probabilistic Optimization of Machine Learning Algorithms for Heart Disease Prediction 1 Jaspreet Kaur, Bharti Joshi and Rajashree Shedge
1.1 Introduction 2
1.1.1 Scope and Motivation 3
1.2 Literature Review 4
1.2.1 Comparative Analysis 5
1.2.2 Survey Analysis 5
1.3 Tools and Techniques 10
1.3.1 Description of Dataset 11
1.3.2 Machine Learning Algorithm 12
1.3.3 Decision Tree 14
1.3.4 Random Forest 15
1.3.5 Naive Bayes Algorithm 16
1.3.6 K Means Algorithm 18
1.3.7 Ensemble Method 18
1.3.7.1 Bagging 19
1.3.7.2 Boosting 19
1.3.7.3 Stacking 19
1.3.7.4 Majority Vote 19
1.4 Proposed Method 20
1.4.1 Experiment and Analysis 20
1.4.2 Method 22
1.5 Conclusion 25
References 26
2 Cancerous Cells Detection in Lung Organs of Human Body: IoT-Based Healthcare 4.0 Approach 29 Rohit Rastogi, D.K. Chaturvedi, Sheelu Sagar, Neeti Tandon and Mukund Rastogi
2.1 Introduction 30
2.1.1 Motivation to the Study 30
2.1.1.1 Problem Statements 31
2.1.1.2 Authors' Contributions 31
2.1.1.3 Research Manuscript Organization 31
2.1.1.4 Definitions 32
2.1.2 Computer-Aided Diagnosis System (CADe or CADx) 32
2.1.3 Sensors for the Internet of Things 32
2.1.4 Wireless and Wearable Sensors for Health Informatics 33
2.1.5 Remote Human's Health and Activity Monitoring 33
2.1.6 Decision-Making Systems for Sensor Data 33
2.1.7 Artificial Intelligence and Machine Learning for Health Informatics 34
2.1.8 Health Sensor Data Management 34
2.1.9 Multimodal Data Fusion for Healthcare 35
2.1.10 Heterogeneous Data Fusion and Context-Aware Systems: A Context-Aware Data Fusion Approach for Health-IoT 35
2.2 Literature Review 35
2.3 Proposed Systems 37
2.3.1 Framework or Architecture of the Work 38
2.3.2 Model Steps and Parameters 38
2.3.3 Discussions 39
2.4 Experimental Results and Analysis 39
2.4.1 Tissue Characterization and Risk Stratification 39
2.4.2 Samples of Cancer Data and Analysis 40
2.5 Novelties 42
2.6 Future Scope, Limitations, and Possible Applications 42
2.7 Recommendations and Consideration 43
2.8 Conclusions 43
References 43
3 Computational Predictors of the Predominant Protein Function: SARS-CoV-2 Case 47 Carlos Polanco, Manlio F. Márquez and Gilberto Vargas-Alarcón
3.1 Introduction 48
3.2 Human Coronavirus Types 49
3.3 The SARS-CoV-2 Pandemic Impact 50
3.3.1 RNA Virus vs DNA Virus 51
3.3.2 The Coronaviridae Family 51
3.3.3 The SARS-CoV-2 Structural Proteins 52
3.3.4 Protein Representations 52
3.4 Computational Predictors 53
3.4.1 Supervised Algorithms 53
3.4.2 Non-Supervised Algorithms 54
3.5 Polarity Index Method(r) 54
3.5.1 The PIM(r) Profile 54
3.5.2 Advantages 55
3.5.3 Disadvantages 55
3.5.4 SARS-CoV-2 Recognition Using PIM(r) Profile 55
3.6 Future Implications 59
3.7 Acknowledgments 60
References 60
4 Deep Learning in Gait Abnormality Detection: Principles and Illustrations 63 Saikat Chakraborty, Sruti Sambhavi and Anup Nandy
