Details

Machine Learning for Healthcare Applications


Machine Learning for Healthcare Applications


1. Aufl.

von: Sachi Nandan Mohanty, G. Nalinipriya, Om Prakash Jena, Achyuth Sarkar

197,99 €

Verlag: Wiley
Format: EPUB
Veröffentl.: 04.05.2021
ISBN/EAN: 9781119792598
Sprache: englisch
Anzahl Seiten: 416

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Beschreibungen

<p>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.</p> <p>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.</p> <p>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.</p>
<p>Preface xvii</p> <p><b>Part 1: Introduction to Intelligent Healthcare Systems 1</b></p> <p><b>1 Innovation on Machine Learning in Healthcare Services—An Introduction 3<br /></b><i>Parthasarathi Pattnayak and Om Prakash Jena</i></p> <p>1.1 Introduction 3</p> <p>1.2 Need for Change in Healthcare 5</p> <p>1.3 Opportunities of Machine Learning in Healthcare 6</p> <p>1.4 Healthcare Fraud 7</p> <p>1.4.1 Sorts of Fraud in Healthcare 7</p> <p>1.4.2 Clinical Service Providers 8</p> <p>1.4.3 Clinical Resource Providers 8</p> <p>1.4.4 Protection Policy Holders 8</p> <p>1.4.5 Protection Policy Providers 9</p> <p>1.5 Fraud Detection and Data Mining in Healthcare 9</p> <p>1.5.1 Data Mining Supervised Methods 10</p> <p>1.5.2 Data Mining Unsupervised Methods 10</p> <p>1.6 Common Machine Learning Applications in Healthcare 10</p> <p>1.6.1 Multimodal Machine Learning for Data Fusion in Medical Imaging 11</p> <p>1.6.2 Machine Learning in Patient Risk Stratification 11</p> <p>1.6.3 Machine Learning in Telemedicine 11</p> <p>1.6.4 AI (ML) Application in Sedate Revelation 12</p> <p>1.6.5 Neuroscience and Image Computing 12</p> <p>1.6.6 Cloud Figuring Systems in Building AI-Based Healthcare 12</p> <p>1.6.7 Applying Internet of Things and Machine-Learning for Personalized Healthcare 12</p> <p>1.6.8 Machine Learning in Outbreak Prediction 13</p> <p>1.7 Conclusion 13</p> <p>References 14</p> <p><b>Part 2: Machine Learning/Deep Learning-Based Model Development 17</b></p> <p><b>2 A Framework for Health Status Estimation Based on Daily Life Activities Data Using Machine Learning Techniques 19<br /></b><i>Tene Ramakrishnudu, T. Sai Prasen and V. Tharun Chakravarthy</i></p> <p>2.1 Introduction 19</p> <p>2.1.1 Health Status of an Individual 19</p> <p>2.1.2 Activities and Measures of an Individual 20</p> <p>2.1.3 Traditional Approach to Predict Health Status 20</p> <p>2.2 Background 20</p> <p>2.3 Problem Statement 21</p> <p>2.4 Proposed Architecture 22</p> <p>2.4.1 Pre-Processing 22</p> <p>2.4.2 Phase-I 23</p> <p>2.4.3 Phase-II 23</p> <p>2.4.4 Dataset Generation 23</p> <p>2.4.4.1 Rules Collection 23</p> <p>2.4.4.2 Feature Selection 24</p> <p>2.4.4.3 Feature Reduction 24</p> <p>2.4.4.4 Dataset Generation From Rules 24</p> <p>2.4.4.5 Example 24</p> <p>2.4.5 Pre-Processing 26</p> <p>2.5 Experimental Results 27</p> <p>2.5.1 Performance Metrics 27</p> <p>2.5.1.1 Accuracy 27</p> <p>2.5.1.2 Precision 28</p> <p>2.5.1.3 Recall 28</p> <p>2.5.1.4 F1-Score 30</p> <p>2.6 Conclusion 31</p> <p>References 31</p> <p><b>3 Study of Neuromarketing With EEG Signals and Machine Learning Techniques 33<br /></b><i>S. Pal, P. Das, R. Sahu and S.R. Dash</i></p> <p>3.1 Introduction 34</p> <p>3.1.1 Why BCI 34</p> <p>3.1.2 Human–Computer Interfaces 34</p> <p>3.1.3 What is EEG 35</p> <p>3.1.4 History of EEG 35</p> <p>3.1.5 About Neuromarketing 35</p> <p>3.1.6 About Machine Learning 36</p> <p>3.2 Literature Survey 36</p> <p>3.3 Methodology 45</p> <p>3.3.1 Bagging Decision Tree Classifier 45</p> <p>3.3.2 Gaussian Naïve Bayes Classifier 45</p> <p>3.3.3 Kernel Support Vector Machine (Sigmoid) 45</p> <p>3.3.4 Random Decision Forest Classifier 46</p> <p>3.4 System Setup & Design 46</p> <p>3.4.1 Pre-Processing & Feature Extraction 47</p> <p>3.4.1.1 Savitzky–Golay Filter 47</p> <p>3.4.1.2 Discrete Wavelet Transform 48</p> <p>3.4.2 Dataset Description 49</p> <p>3.5 Result 49</p> <p>3.5.1 Individual Result Analysis 49</p> <p>3.5.2 Comparative Results Analysis 52</p> <p>3.6 Conclusion 53</p> <p>References 54</p> <p><b>4 An Expert System-Based Clinical Decision Support System for Hepatitis-B Prediction & Diagnosis 57<br /></b><i>Niranjan Panigrahi, Ishan Ayus and Om Prakash Jena</i></p> <p>4.1 Introduction 57</p> <p>4.2 Outline of Clinical DSS 59</p> <p>4.2.1 Preliminaries 59</p> <p>4.2.2 Types of Clinical DSS 60</p> <p>4.2.3 Non-Knowledge-Based Decision Support System (NK-DSS) 60</p> <p>4.2.4 Knowledge-Based Decision Support System (K-DSS) 62</p> <p>4.2.5 Hybrid Decision Support System (H-DSS) 64</p> <p>4.2.6 DSS Architecture 64</p> <p>4.3 Background 65</p> <p>4.4 Proposed Expert System-Based CDSS 65</p> <p>4.4.1 Problem Description 65</p> <p>4.4.2 Rules Set & Knowledge Base 66</p> <p>4.4.3 Inference Engine 66</p> <p>4.5 Implementation & Testing 66</p> <p>4.6 Conclusion 73</p> <p>References 73</p> <p><b>5 Deep Learning on Symptoms in Disease Prediction 77<br /></b><i>Sheikh Raul Islam, Rohit Sinha, Santi P. Maity and Ajoy Kumar Ray</i></p> <p>5.1 Introduction 77</p> <p>5.2 Literature Review 78</p> <p>5.3 Mathematical Models 79</p> <p>5.3.1 Graphs and Related Terms 80</p> <p>5.3.2 Deep Learning in Graph 80</p> <p>5.3.3 Network Embedding 80</p> <p>5.3.4 Graph Neural Network 81</p> <p>5.3.5 Graph Convolution Network 82</p> <p>5.4 Learning Representation From DSN 82</p> <p>5.4.1 Description of the Proposed Model 83</p> <p>5.4.2 Objective Function 84</p> <p>5.5 Results and Discussion 84</p> <p>5.5.1 Description of the Dataset 85</p> <p>5.5.2 Training Progress 85</p> <p>5.5.3 Performance Comparisons 86</p> <p>5.6 Conclusions and Future Scope 86</p> <p>References 87</p> <p><b>6 Intelligent Vision-Based Systems for Public Safety and Protection via Machine Learning Techniques 89<br /></b><i>Rajitha B.</i></p> <p>6.1 Introduction 89</p> <p>6.1.1 Problems Intended in Video Surveillance Systems 90</p> <p>6.1.2 Current Developments in This Area 91</p> <p>6.1.3 Role of AI in Video Surveillance Systems 91</p> <p>6.2 Public Safety and Video Surveillance Systems 92</p> <p>6.2.1 Offline Crime Prevention 92</p> <p>6.2.2 Crime Prevention and Identification via Apps 92</p> <p>6.2.3 Crime Prevention and Identification via CCTV 92</p> <p>6.3 Machine Learning for Public Safety 94</p> <p>6.3.1 Abnormality Behavior Detection via Deep Learning 95</p> <p>6.3.2 Video Analytics Methods for Accident Classification/Detection 97</p> <p>6.3.3 Feature Selection and Fusion Methods 98</p> <p>6.4 Securing the CCTV Data 99</p> <p>6.4.1 Image/Video Security Challenges 99</p> <p>6.4.2 Blockchain for Image/Video Security 99</p> <p>6.5 Conclusion 99</p> <p>References 100</p> <p><b>7 Semantic Framework in Healthcare 103<br /></b><i>Sankar Pariserum Perumal, Ganapathy Sannasi, Selvi M. and Kannan Arputharaj</i></p> <p>7.1 Introduction 103</p> <p>7.2 Semantic Web Ontology 104</p> <p>7.3 Multi-Agent System in a Semantic Framework 106</p> <p>7.3.1 Existing Healthcare Semantic Frameworks 107</p> <p>7.3.1.1 AOIS 107</p> <p>7.3.1.2 SCKE 108</p> <p>7.3.1.3 MASE 109</p> <p>7.3.1.4 MET4 110</p> <p>7.3.2 Proposed Multi-Agent-Based Semantic Framework for Healthcare Instance Data 111</p> <p>7.3.2.1 Data Dictionary 111</p> <p>7.3.2.2 Mapping Database 112</p> <p>7.3.2.3 Decision Making Ontology 113</p> <p>7.3.2.4 STTL and SPARQL-Based RDF Transformation 115</p> <p>7.3.2.5 Query Optimizer Agent 116</p> <p>7.3.2.6 Semantic Web Services Ontology 116</p> <p>7.3.2.7 Web Application User Interface and Customer Agent 116</p> <p>7.3.2.8 Translation Agent 117</p> <p>7.3.2.9 RDF Translator 117</p> <p>7.4 Conclusion 118</p> <p>References 119</p> <p><b>8 Detection, Prediction & Intervention of Attention Deficiency in the Brain Using tDCS 121<br /></b><i>Pallabjyoti Kakoti, Rissnalin Syiemlieh and Eeshankur Saikia</i></p> <p>8.1 Introduction 121</p> <p>8.2 Materials & Methods 123</p> <p>8.2.1 Subjects and Experimental Design 123</p> <p>8.2.2 Data Pre-Processing & Statistical Analysis 125</p> <p>8.2.3 Extracting Singularity Spectrum from EEG 126</p> <p>8.3 Results & Discussion 126</p> <p>8.4 Conclusion 132</p> <p>Acknowledgement 133</p> <p>References 133</p> <p><b>9 Detection of Onset and Progression of Osteoporosis Using Machine Learning 137<br /></b><i>Shilpi Ruchi Kerketta and Debalina Ghosh</i></p> <p>9.1 Introduction 137</p> <p>9.1.1 Measurement Techniques of BMD 138</p> <p>9.1.2 Machine Learning Algorithms in Healthcare 138</p> <p>9.1.3 Organization of Chapter 139</p> <p>9.2 Microwave Characterization of Human Osseous Tissue 139</p> <p>9.2.1 Frequency-Domain Analysis of Human Wrist Sample 140</p> <p>9.2.2 Data Collection and Analysis 141</p> <p>9.3 Prediction Model of Osteoporosis Using Machine Learning Algorithms 144</p> <p>9.3.1 K-Nearest Neighbor (KNN) 144</p> <p>9.3.2 Decision Tree 145</p> <p>9.3.3 Random Forest 145</p> <p>9.4 Conclusion 148</p> <p>Acknowledgment 148</p> <p>References 148</p> <p><b>10 Applications of Machine Learning in Biomedical Text Processing and Food Industry 151<br /></b><i>K. Paramesha, Gururaj H.L. and Om Prakash Jena</i></p> <p>10.1 Introduction 152</p> <p>10.2 Use Cases of AI and ML in Healthcare 153</p> <p>10.2.1 Speech Recognition (SR) 153</p> <p>10.2.2 Pharmacovigilance and Adverse Drug Effects (ADE) 153</p> <p>10.2.3 Clinical Imaging and Diagnostics 153</p> <p>10.2.4 Conversational AI in Healthcare 154</p> <p>10.3 Use Cases of AI and ML in Food Technology 154</p> <p>10.3.1 Assortment of Vegetables and Fruits 154</p> <p>10.3.2 Personal Hygiene 154</p> <p>10.3.3 Developing New Products 155</p> <p>10.3.4 Plant Leaf Disease Detection 156</p> <p>10.3.5 Face Recognition Systems for Domestic Cattle 156</p> <p>10.3.6 Cleaning Processing Equipment 157</p> <p>10.4 A Case Study: Sentiment Analysis of Drug Reviews 158</p> <p>10.4.1 Dataset 159</p> <p>10.4.2 Approaches for Sentiment Analysis on Drug Reviews 159</p> <p>10.4.3 BoW and TF-IDF Model 160</p> <p>10.4.4 Bi-LSTM Model 160</p> <p>10.4.4.1 Word Embedding 160</p> <p>10.4.5 Deep Learning Model 161</p> <p>10.5 Results and Analysis 164</p> <p>10.6 Conclusion 165</p> <p>References 166</p> <p><b>11 Comparison of MobileNet and ResNet CNN Architectures in the CNN-Based Skin Cancer Classifier Model 169<br /></b><i>Subasish Mohapatra, N.V.S. Abhishek, Dibyajit Bardhan, Anisha Ankita Ghosh and Shubhadarshinin Mohanty</i></p> <p>11.1 Introduction 169</p> <p>11.2 Our Skin Cancer Classifier Model 171</p> <p>11.3 Skin Cancer Classifier Model Results 172</p> <p>11.4 Hyperparameter Tuning and Performance 174</p> <p>11.4.1 Hyperparameter Tuning of MobileNet-Based CNN Model 175</p> <p>11.4.2 Hyperparameter Tuning of ResNet50-Based CNN Model 175</p> <p>11.4.3 Table Summary of Hyperparameter Tuning Results 176</p> <p>11.5 Comparative Analysis and Results 176</p> <p>11.5.1 Training and Validation Loss 177</p> <p>11.5.1.1 MobileNet 177</p> <p>11.5.1.2 ResNet50 177</p> <p>11.5.1.3 Inferences 177</p> <p>11.5.2 Training and Validation Categorical Accuracy 178</p> <p>11.5.2.1 MobileNet 178</p> <p>11.5.2.2 ResNet50 178</p> <p>11.5.2.3 Inferences 178</p> <p>11.5.3 Training and Validation Top 2 Accuracy 179</p> <p>11.5.3.1 MobileNet 179</p> <p>11.5.3.2 ResNet50 179</p> <p>11.5.3.3 Inferences 180</p> <p>11.5.4 Training and Validation Top 3 Accuracy 180</p> <p>11.5.4.1 MobileNet 180</p> <p>11.5.4.2 ResNet50 180</p> <p>11.5.4.3 Inferences 181</p> <p>11.5.5 Confusion Matrix 181</p> <p>11.5.5.1 MobileNet 181</p> <p>11.5.5.2 ResNet50 181</p> <p>11.5.5.3 Inferences 182</p> <p>11.5.6 Classification Report 182</p> <p>11.5.6.1 MobileNet 182</p> <p>11.5.6.2 ResNet50 182</p> <p>11.5.6.3 Inferences 183</p> <p>11.5.7 Last Epoch Results 183</p> <p>11.5.7.1 MobileNet 183</p> <p>11.5.7.2 ResNet50 183</p> <p>11.5.7.3 Inferences 184</p> <p>11.5.8 Best Epoch Results 184</p> <p>11.5.8.1 MobileNet 184</p> <p>11.5.8.2 ResNet50 184</p> <p>11.5.8.3 Inferences 184</p> <p>11.5.9 Overall Comparative Analysis 184</p> <p>11.6 Conclusion 185</p> <p>References 185</p> <p><b>12 Deep Learning-Based Image Classifier for Malaria Cell Detection 187<br /></b><i>Alok Negi, Krishan Kumar and Prachi Chauhan</i></p> <p>12.1 Introduction 187</p> <p>12.2 Related Work 189</p> <p>12.3 Proposed Work 190</p> <p>12.3.1 Dataset Description 191</p> <p>12.3.2 Data Pre-Processing and Augmentation 191</p> <p>12.3.3 CNN Architecture and Implementation 192</p> <p>12.4 Results and Evaluation 194</p> <p>12.5 Conclusion 196</p> <p>References 197</p> <p><b>13 Prediction of Chest Diseases Using Transfer Learning 199<br /></b><i>S. Baghavathi Priya, M. Rajamanogaran and S. Subha</i></p> <p>13.1 Introduction 199</p> <p>13.2 Types of Diseases 200</p> <p>13.2.1 Pneumothorax 200</p> <p>13.2.2 Pneumonia 200</p> <p>13.2.3 Effusion 200</p> <p>13.2.4 Atelectasis 201</p> <p>13.2.5 Nodule and Mass 202</p> <p>13.2.6 Cardiomegaly 202</p> <p>13.2.7 Edema 202</p> <p>13.2.8 Lung Consolidation 202</p> <p>13.2.9 Pleural Thickening 202</p> <p>13.2.10 Infiltration 202</p> <p>13.2.11 Fibrosis 203</p> <p>13.2.12 Emphysema 203</p> <p>13.3 Diagnosis of Lung Diseases 204</p> <p>13.4 Materials and Methods 204</p> <p>13.4.1 Data Augmentation 206</p> <p>13.4.2 CNN Architecture 206</p> <p>13.4.3 Lung Disease Prediction Model 207</p> <p>13.5 Results and Discussions 208</p> <p>13.5.1 Implementation Results Using ROC Curve 209</p> <p>13.6 Conclusion 210</p> <p>References 212</p> <p><b>14 Early Stage Detection of Leukemia Using Artificial Intelligence 215<br /></b><i>Neha Agarwal and Piyush Agrawal</i></p> <p>14.1 Introduction 215</p> <p>14.1.1 Classification of Leukemia 216</p> <p>14.1.1.1 Acute Lymphocytic Leukemia 216</p> <p>14.1.1.2 Acute Myeloid Leukemia 216</p> <p>14.1.1.3 Chronic Lymphocytic Leukemia 216</p> <p>14.1.1.4 Chronic Myeloid Leukemia 216</p> <p>14.1.2 Diagnosis of Leukemia 216</p> <p>14.1.3 Acute and Chronic Stages of Leukemia 217</p> <p>14.1.4 The Role of AI in Leukemia Detection 217</p> <p>14.2 Literature Review 219</p> <p>14.3 Proposed Work 220</p> <p>14.3.1 Modules Involved in Proposed Methodology 221</p> <p>14.3.2 Flowchart 222</p> <p>14.3.3 Proposed Algorithm 223</p> <p>14.4 Conclusion and Future Aspects 223</p> <p>References 223</p> <p><b>Part 3: Internet of Medical Things (IoMT) for Healthcare 225</b></p> <p><b>15 IoT Application in Interconnected Hospitals 227<br /></b><i>Subhra Debdas, Chinmoy Kumar Panigrahi, Priyasmita Kundu, Sayantan Kundu and Ramanand Jha</i></p> <p>15.1 Introduction 228</p> <p>15.2 Networking Systems Using IoT 229</p> <p>15.3 What are Smart Hospitals? 233</p> <p>15.3.1 Environment of a Smart Hospital 234</p> <p>15.4 Assets 236</p> <p>15.4.1 Overview of Smart Hospital Assets 236</p> <p>15.4.2 Exigency of Automated Healthcare Center Assets 239</p> <p>15.5 Threats 241</p> <p>15.5.1 Emerging Vulnerabilities 241</p> <p>15.5.2 Threat Analysis 244</p> <p>15.6 Conclusion 246</p> <p>References 246</p> <p><b>16 Real Time Health Monitoring Using IoT With Integration of Machine Learning Approach 249<br /></b><i>K.G. Maheswari, G. Nalinipriya, C. Siva and A. Thilakesh Raj</i></p> <p>16.1 Introduction 250</p> <p>16.2 Related Work 250</p> <p>16.3 Existing Healthcare Monitoring System 251</p> <p>16.4 Methodology and Data Analysis 251</p> <p>16.5 Proposed System Architecture 252</p> <p>16.6 Machine Learning Approach 252</p> <p>16.6.1 Multiple Linear Regression Algorithm 253</p> <p>16.6.2 Random Forest Algorithm 253</p> <p>16.6.3 Support Vector Machine 253</p> <p>16.7 Work Flow of the Proposed System 253</p> <p>16.8 System Design of Health Monitoring System 256</p> <p>16.9 Use Case Diagram 257</p> <p>16.10 Conclusion 258</p> <p>References 259</p> <p><b>Part 4: Machine Learning Applications for COVID-19 261</b></p> <p><b>17 Semantic and NLP-Based Retrieval From Covid-19 Ontology 263<br /></b><i>Ramar Kaladevi and Appavoo Revathi</i></p> <p>17.1 Introduction 263</p> <p>17.2 Related Work 264</p> <p>17.3 Proposed Retrieval System 266</p> <p>17.3.1 Why Ontology? 266</p> <p>17.3.2 Covid Ontology 266</p> <p>17.3.3 Information Retrieval From Ontology 269</p> <p>17.3.4 Query Formulation 272</p> <p>17.3.5 Retrieval From Knowledgebase 272</p> <p>17.4 Conclusion 273</p> <p>References 273</p> <p><b>18 Semantic Behavior Analysis of COVID-19 Patients: A Collaborative Framework 277<br /></b><i>Amlan Mohanty, Debasish Kumar Mallick, Shantipriya Parida and Satya Ranjan Dash</i></p> <p>18.1 Introduction 278</p> <p>18.2 Related Work 280</p> <p>18.2.1 Semantic Analysis and Topic Discovery of Alcoholic Patients From Social Media Platforms 280</p> <p>18.2.2 Sentiment Analysis of Tweets From Twitter Handles of the People of Nepal in Response to the COVID-19 Pandemic 280</p> <p>18.2.3 Study of Sentiment Analysis and Analyzing Scientific Papers 280</p> <p>18.2.4 Informatics and COVID-19 Research 281</p> <p>18.2.5 COVID-19 Outbreak in the World and Twitter Sentiment Analysis 281</p> <p>18.2.6 LDA Topic Modeling on Twitter to Study Public Discourse and Sentiment During the Coronavirus Pandemic 281</p> <p>18.2.7 The First Decade of Research on Sentiment Analysis 282</p> <p>18.2.8 Detailed Survey on the Semantic Analysis Techniques for NLP 282</p> <p>18.2.9 Understanding Text Semantics With LSA 282</p> <p>18.2.10 Analyzing Suicidal Tendencies With Semantic Analysis Using Social Media 283</p> <p>18.2.11 Analyzing Public Opinion on BREXIT Using Sentiment Analysis 283</p> <p>18.2.12 Prediction of Indian Elections Using NLP and Decision Tree 283</p> <p>18.3 Methodology 283</p> <p>18.4 Conclusion 286</p> <p>References 287</p> <p><b>19 Comparative Study of Various Data Mining Techniques Towards Analysis and Prediction of Global COVID-19 Dataset 289<br /></b><i>Sachin Kamley</i></p> <p>19.1 Introduction 289</p> <p>19.2 Literature Review 290</p> <p>19.3 Materials and Methods 292</p> <p>19.3.1 Dataset Collection 292</p> <p>19.3.2 Support Vector Machine (SVM) 292</p> <p>19.3.3 Decision Tree (DT) 294</p> <p>19.3.4 K-Means Clustering 294</p> <p>19.3.5 Back Propagation Neural Network (BPNN) 295</p> <p>19.4 Experimental Results 296</p> <p>19.5 Conclusion and Future Scopes 305</p> <p>References 306</p> <p><b>20 Automated Diagnosis of COVID-19 Using Reinforced Lung Segmentation and Classification Model 309<br /></b><i>J. Shiny Duela and T. Illakiya</i></p> <p>20.1 Introduction 309</p> <p>20.2 Diagnosis of COVID-19 310</p> <p>20.2.1 Pre-Processing of Lung CT Image 310</p> <p>20.2.2 Lung CT Image Segmentation 311</p> <p>20.2.3 ROI Extraction 311</p> <p>20.2.4 Feature Extraction 311</p> <p>20.2.5 Classification 311</p> <p>20.3 Genetic Algorithm (GA) 311</p> <p>20.3.1 Operators of GA 312</p> <p>20.3.2 Applications of GA 312</p> <p>20.4 Related Works 313</p> <p>20.5 Challenges in GA 314</p> <p>20.6 Challenges in Lung CT Segmentation 314</p> <p>20.7 Proposed Diagnosis Framework 314</p> <p>20.7.1 Image Pre-Processing 315</p> <p>20.7.2 Proposed Image Segmentation Technique 315</p> <p>20.7.3 ROI Segmentation 318</p> <p>20.7.4 Feature Extraction 318</p> <p>20.7.5 Modified GA Classifier 318</p> <p>20.7.5.1 Gaussian Type—II Fuzzy in Classification 318</p> <p>20.7.5.2 Classifier Algorithm 319</p> <p>20.8 Result Discussion 319</p> <p>20.9 Conclusion 321</p> <p>References 321</p> <p><b>Part 5: Case Studies of Application Areas of Machine Learning in Healthcare System 323</b></p> <p><b>21 Future of Telemedicine with ML: Building a Telemedicine Framework for Lung Sound Detection 325<br /></b>Sudhansu Shekhar Patra, Nitin S. Goje, Kamakhya Narain Singh, Kaish Q. Khan, Deepak Kumar, Madhavi and Kumar Ashutosh Sharma</p> <p>21.1 Introduction 325</p> <p>21.1.1 Monitoring the Remote Patient 326</p> <p>21.1.2 Intelligent Assistance for Patient Diagnosis 326</p> <p>21.1.3 Fasten Electronic Health Record Retrieval Process 326</p> <p>21.1.4 Collaboration Increases Among Healthcare Practitioners 326</p> <p>21.2 Related Work 327</p> <p>21.3 Strategic Model for Telemedicine 328</p> <p>21.4 Framework for Lung Sound Detection in Telemedicine 330</p> <p>21.4.1 Data Collection 330</p> <p>21.4.2 Pre-Processing of Data 331</p> <p>21.4.3 Feature Extraction 331</p> <p>21.4.3.1 MFCC 331</p> <p>21.4.3.2 Lung Sounds Using Multi Resolution DWT 332</p> <p>21.4.4 Classification 334</p> <p>21.4.4.1 Correlation Coefficient for Feature Selection (CFS) 334</p> <p>21.4.4.2 Symmetrical Uncertainty 334</p> <p>21.4.4.3 Gain Ratio 335</p> <p>21.4.4.4 Modified RF Classification Architecture 335</p> <p>21.5 Experimental Analysis 335</p> <p>21.6 Conclusion 340</p> <p>References 340</p> <p><b>22 A Lightweight Convolutional Neural Network Model for Tuberculosis Bacilli Detection From Microscopic Sputum Smear Images 343<br /></b><i>Rani Oomman Panicker, S.J. Pawan, Jeny Rajan and M.K. Sabu</i></p> <p>22.1 Introduction 343</p> <p>22.2 Literature Review 345</p> <p>22.3 Proposed Work 346</p> <p>22.4 Experimental Results and Discussion 349</p> <p>22.5 Conclusion 350</p> <p>References 350</p> <p><b>23 Role of Machine Learning and Texture Features for the Diagnosis of Laryngeal Cancer 353<br /></b><i>Vibhav Prakash Singh and Ashish Kumar Maurya</i></p> <p>23.1 Introduction 353</p> <p>23.2 Clinically Correlated Texture Features 358</p> <p>23.2.1 Texture-Based LBP Descriptors 358</p> <p>23.2.2 GLCM Features 358</p> <p>23.2.3 Statistical Features 359</p> <p>23.3 Machine Learning Techniques 359</p> <p>23.3.1 Support Vector Machine (SVM) 359</p> <p>23.3.2 k-NN (k-Nearest Neighbors) 360</p> <p>23.3.3 Random Forest (RF) 361</p> <p>23.3.4 Naïve Bayes 361</p> <p>23.4 Result Analysis and Discussions 361</p> <p>23.5 Conclusions 366</p> <p>References 366</p> <p><b>24 Analysis of Machine Learning Technologies for the Detection of Diabetic Retinopathy 369<br /></b><i>Biswabijayee Chandra Sekhar Mohanty, Sonali Mishra and Sambit Kumar Mishra</i></p> <p>24.1 Introduction 369</p> <p>24.2 Related Work 370</p> <p>24.2.1 Pre-Processing of Image 371</p> <p>24.2.2 Diabetic Retinopathy Detection 372</p> <p>24.2.3 Grading of DR 374</p> <p>24.3 Dataset Used 374</p> <p>24.3.1 DIARETDB1 374</p> <p>24.3.2 Diabetic-Retinopathy-Detection Dataset 376</p> <p>24.4 Methodology Used 377</p> <p>24.4.1 Pre-Processing 377</p> <p>24.4.2 Segmentation 377</p> <p>24.4.3 Feature Extraction 378</p> <p>24.4.4 Classification 378</p> <p>24.5 Analysis of Results and Discussion 379</p> <p>24.6 Conclusion 380</p> <p>References 381</p> <p>Index 383</p>
<p><b>Sachi Nandan Mohanty</b> 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.</p><p><b>G. Nalinipriya</b> 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.</p><p><b>Om Prakash Jena</b> 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.</p><p><b>Achyuth Sarkar</b> 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.</p>
<p><b>This book elucidates different dimensions of machine learning applications and illustrates its use in solutions of assorted real world biomedical and healthcare problems.</b></p><p>Machine learning is one of the principal components of computational methodology. In today’s highly integrated world, when solutions to problems are cross-disciplinary in nature, machine learning promises to become a powerful means for obtaining solutions to problems very quickly, yet accurately and acceptably.</p><p>The approach of this book 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. Since this book is intended to be useful to a wide audience, 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.</p><p><i>Machine Learning for Healthcare Applications</i> comprises 22 real-time innovative chapters providing a comprehensive overview of current technology. Each of these chapters specifies requirements and provides a description of both the chosen approach and its implementation.</p><p><b>Audience</b></p><p>The book will be read by scientists and engineers in artificial intelligence, information technology, bioinformatics as well as specialist stakeholders in the biomedical sector such as hospitals & healthcare providers, pharmaceutical & biotechnology companies, medical imaging & diagnostics centers, healthcare assistance robots manufacturers, telehealth companies.</p>

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