Biomedical data mining for information retrieval : methodologies, techniques, and applications /: methodologies, techniques, and applications. (2021)

• Record Type: Book
• Further Information: Note: Edited by Subhendu Kumar Pani, Sujata Dash, S. Balamurugan, Ajith Abraham.
• Editors: Pani, Subhendu Kumar, 1980-; Dash, Sujata, 1964-; Prof, Balamurugan, S; Abraham, Ajith, 1968-
• Contents: Preface xv 1 Mortality Prediction of ICU Patients Using Machine Learning Techniques 1; Babita Majhi, Aarti Kashyap and Ritanjali Majhi 1.1 Introduction 2 1.2 Review of Literature 3 1.3 Materials and Methods 8 1.3.1 Dataset 8 1.3.2 Data Pre-Processing 8 1.3.3 Normalization 8 1.3.4 Mortality Prediction 10 1.3.5 Model Description and Development 11 1.4 Result and Discussion 15 1.5 Conclusion 16 1.6 Future Work 16 References 17 2 Artificial Intelligence in Bioinformatics 21; V. Samuel Raj, Anjali Priyadarshini, Manoj Kumar Yadav, Ramendra Pati Pandey, Archana Gupta and Arpana Vibhuti 2.1 Introduction 21 2.2 Recent Trends in the Field of AI in Bioinformatics 22 2.2.1 DNA Sequencing and Gene Prediction Using Deep Learning 24 2.3 Data Management and Information Extraction 26 2.4 Gene Expression Analysis 26 2.4.1 Approaches for Analysis of Gene Expression 27 2.4.2 Applications of Gene Expression Analysis 29 2.5 Role of Computation in Protein Structure Prediction 30 2.6 Application in Protein Folding Prediction 31 2.7 Role of Artificial Intelligence in Computer-Aided Drug Design 38 2.8 Conclusions 42 References 43 3 Predictive Analysis in Healthcare Using Feature Selection 53; Aneri Acharya, Jitali Patel and Jigna Patel 3.1 Introduction 54 3.1.1 Overview and Statistics About the Disease 54 3.1.1.1 Diabetes 54 3.1.1.2 Hepatitis 55 3.1.2 Overview of the Experiment Carried Out 56 3.2 Literature Review 58 3.2.1 Summary 58 3.2.2 Comparison of Papers for Diabetes and Hepatitis Dataset 61 3.3 Dataset Description 70 3.3.1 Diabetes Dataset 70 3.3.2 Hepatitis Dataset 71 3.4 Feature Selection 73 3.4.1 Importance of Feature Selection 74 3.4.2 Difference Between Feature Selection, Feature Extraction and Dimensionality Reduction 74 3.4.3 Why Traditional Feature Selection Techniques Still Holds True? 75 3.4.4 Advantages and Disadvantages of Feature Selection Technique 76 3.4.4.1 Advantages 76 3.4.4.2 Disadvantage 76 3.5 Feature Selection Methods 76 3.5.1 Filter Method 76 3.5.1.1 Basic Filter Methods 77 3.5.1.2 Correlation Filter Methods 77 3.5.1.3 Statistical & Ranking Filter Methods 78 3.5.1.4 Advantages and Disadvantages of Filter Method 80 3.5.2 Wrapper Method 80 3.5.2.1 Advantages and Disadvantages of Wrapper Method 82 3.5.2.2 Difference Between Filter Method and Wrapper Method 82 3.6 Methodology 84 3.6.1 Steps Performed 84 3.6.2 Flowchart 84 3.7 Experimental Results and Analysis 85 3.7.1 Task 1—Application of Four Machine Learning Models 85 3.7.2 Task 2—Applying Ensemble Learning Algorithms 86 3.7.3 Task 3—Applying Feature Selection Techniques 87 3.7.4 Task 4—Appling Data Balancing Technique 94 3.8 Conclusion 96 References 99 4 Healthcare 4.0: An Insight of Architecture, Security Requirements, Pillars and Applications 103; Deepanshu Bajaj, Bharat Bhushan and Divya Yadav 4.1 Introduction 104 4.2 Basic Architecture and Components of e-Health Architecture 105 4.2.1 Front End Layer 106 4.2.2 Communication Layer 107 4.2.3 Back End Layer 107 4.3 Security Requirements in Healthcare 4.0 108 4.3.1 Mutual-Authentications 109 4.3.2 Anonymity 110 4.3.3 Un-Traceability 111 4.3.4 Perfect—Forward—Secrecy 111 4.3.5 Attack Resistance 111 4.3.5.1 Replay Attack 111 4.3.5.2 Spoofing Attack 112 4.3.5.3 Modification Attack 112 4.3.5.4 MITM Attack 112 4.3.5.5 Impersonation Attack 112 4.4 ICT Pillar’s Associated With HC4.0 113 4.4.1 IoT in Healthcare 4.0 114 4.4.2 Cloud Computing (CC) in Healthcare 4.0 115 4.4.3 Fog Computing (FC) in Healthcare 4.0 116 4.4.4 BigData (BD) in Healthcare 4.0 117 4.4.5 Machine Learning (ML) in Healthcare 4.0 118 4.4.6 Blockchain (BC) in Healthcare 4.0 120 4.5 Healthcare 4.0’s Applications-Scenarios 121 4.5.1 Monitor-Physical and Pathological Related Signals 121 4.5.2 Self-Management, and Wellbeing Monitor, and its Precaution 124 4.5.3 Medication Consumption Monitoring and Smart-Pharmaceutics 124 4.5.4 Personalized (or Customized) Healthcare 125 4.5.5 Cloud-Related Medical Information’s Systems 125 4.5.6 Rehabilitation 126 4.6 Conclusion 126 References 127 5 Improved Social Media Data Mining for Analyzing Medical Trends 131; Minakshi Sharma and Sunil Sharma 5.1 Introduction 132 5.1.1 Data Mining 132 5.1.2 Major Components of Data Mining 132 5.1.3 Social Media Mining 134 5.1.4 Clustering in Data Mining 134 5.2 Literature Survey 136 5.3 Basic Data Mining Clustering Technique 140 5.3.1 Classifier and Their Algorithms in Data Mining 143 5.4 Research Methodology 147 5.5 Results and Discussion 151 5.5.1 Tool Description 151 5.5.2 Implementation Results 152 5.5.3 Comparison Graphs Performance Comparison 156 5.6 Conclusion & Future Scope 157 References 158 6 Bioinformatics: An Important Tool in Oncology 163; Gaganpreet Kaur, Saurabh Gupta, Gagandeep Kaur, Manju Verma and Pawandeep Kaur 6.1 Introduction 164 6.2 Cancer—A Brief Introduction 165 6.2.1 Types of Cancer 166 6.2.2 Development of Cancer 166 6.2.3 Properties of Cancer Cells 166 6.2.4 Causes of Cancer 168 6.3 Bioinformatics—A Brief Introduction 169 6.4 Bioinformatics—A Boon for Cancer Research 170 6.5 Applications of Bioinformatics Approaches in Cancer 174 6.5.1 Biomarkers: A Paramount Tool for Cancer Research 175 6.5.2 Comparative Genomic Hybridization for Cancer Research 177 6.5.3 Next-Generation Sequencing 178 6.5.4 miRNA 179 6.5.5 Microarray Technology 181 6.5.6 Proteomics-Based Bioinformatics Techniques 185 6.5.7 Expressed Sequence Tags (EST) and Serial Analysis of Gene Expression (SAGE) 187 6.6 Bioinformatics: A New Hope for Cancer Therapeutics 188 6.7 Conclusion 191 References 192 7 Biomedical Big Data Analytics Using IoT in Health Informatics 197; Pawan Singh Gangwar and Yasha Hasija 7.1 Introduction 198 7.2 Biomedical Big Data 200 7.2.1 Big EHR Data 201 7.2.2 Medical Imaging Data 201 7.2.3 Clinical Text Mining Data 201 7.2.4 Big OMICs Data 202 7.3 Healthcare Internet of Things (IoT) 202 7.3.1 IoT Architecture 202 7.3.2 IoT Data Source 204 7.3.2.1 IoT Hardware 204 7.3.2.2 IoT Middleware 205 7.3.2.3 IoT Presentation 205 7.3.2.4 IoT Software 205 7.3.2.5 IoT Protocols 206 7.4 Studies Related to Big Data Analytics in Healthcare IoT 206 7.5 Challenges for Medical IoT & Big Data in Healthcare 209 7.6 Conclusion 210 References 210 8 Statistical Image Analysis of Drying Bovine Serum Albumin Droplets in Phosphate Buffered Saline 213; Anusuya Pal, Amalesh Gope and Germano S. Iannacchione 8.1 Introduction 214 8.2 Experimental Methods 216 8.3 Results 217 8.3.1 Temporal Study of the Drying Droplets 217 8.3.2 FOS Characterization of the Drying Evolution 219 8.3.3 GLCM Characterization of the Drying
• Edition: 1st
• Publisher Details: Hoboken : Wiley-Scrivener
• Publication Date: 2021
• Extent: 1 online resource
• Subjects: 610.2856312; Medical informatics; Data mining; Biomedical engineering -- Data processing
• Languages: English
• ISBNs: 9781119711261
• Related ISBNs: 9781119711247
• Notes: Note: Description based on CIP data; resource not viewed.
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