Machine Learning for Healthcare Applications

Inbunden, Engelska, 2021

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Beskrivning

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.

Produktinformation

Utgivningsdatum:2021-04-20
Mått:10 x 10 x 10 mm
Vikt:454 g
Format:Inbunden
Språk:Engelska
Antal sidor:416
Förlag:John Wiley & Sons Inc
ISBN:9781119791812

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Mer om författaren

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.

Innehållsförteckning

Preface xviiPart 1: Introduction to Intelligent Healthcare Systems 11 Innovation on Machine Learning in Healthcare Services—An Introduction 3Parthasarathi Pattnayak and Om Prakash Jena1.1 Introduction 31.2 Need for Change in Healthcare 51.3 Opportunities of Machine Learning in Healthcare 61.4 Healthcare Fraud 71.4.1 Sorts of Fraud in Healthcare 71.4.2 Clinical Service Providers 81.4.3 Clinical Resource Providers 81.4.4 Protection Policy Holders 81.4.5 Protection Policy Providers 91.5 Fraud Detection and Data Mining in Healthcare 91.5.1 Data Mining Supervised Methods 101.5.2 Data Mining Unsupervised Methods 101.6 Common Machine Learning Applications in Healthcare 101.6.1 Multimodal Machine Learning for Data Fusion in Medical Imaging 111.6.2 Machine Learning in Patient Risk Stratification 111.6.3 Machine Learning in Telemedicine 111.6.4 AI (ML) Application in Sedate Revelation 121.6.5 Neuroscience and Image Computing 121.6.6 Cloud Figuring Systems in Building AI-Based Healthcare 121.6.7 Applying Internet of Things and Machine-Learning for Personalized Healthcare 121.6.8 Machine Learning in Outbreak Prediction 131.7 Conclusion 13References 14Part 2: Machine Learning/Deep Learning-Based Model Development 172 A Framework for Health Status Estimation Based on Daily Life Activities Data Using Machine Learning Techniques 19Tene Ramakrishnudu, T. Sai Prasen and V. Tharun Chakravarthy2.1 Introduction 192.1.1 Health Status of an Individual 192.1.2 Activities and Measures of an Individual 202.1.3 Traditional Approach to Predict Health Status 202.2 Background 202.3 Problem Statement 212.4 Proposed Architecture 222.4.1 Pre-Processing 222.4.2 Phase-I 232.4.3 Phase-II 232.4.4 Dataset Generation 232.4.4.1 Rules Collection 232.4.4.2 Feature Selection 242.4.4.3 Feature Reduction 242.4.4.4 Dataset Generation From Rules 242.4.4.5 Example 242.4.5 Pre-Processing 262.5 Experimental Results 272.5.1 Performance Metrics 272.5.1.1 Accuracy 272.5.1.2 Precision 282.5.1.3 Recall 282.5.1.4 F1-Score 302.6 Conclusion 31References 313 Study of Neuromarketing With EEG Signals and Machine Learning Techniques 33S. Pal, P. Das, R. Sahu and S.R. Dash3.1 Introduction 343.1.1 Why BCI 343.1.2 Human–Computer Interfaces 343.1.3 What is EEG 353.1.4 History of EEG 353.1.5 About Neuromarketing 353.1.6 About Machine Learning 363.2 Literature Survey 363.3 Methodology 453.3.1 Bagging Decision Tree Classifier 453.3.2 Gaussian Naïve Bayes Classifier 453.3.3 Kernel Support Vector Machine (Sigmoid) 453.3.4 Random Decision Forest Classifier 463.4 System Setup & Design 463.4.1 Pre-Processing & Feature Extraction 473.4.1.1 Savitzky–Golay Filter 473.4.1.2 Discrete Wavelet Transform 483.4.2 Dataset Description 493.5 Result 493.5.1 Individual Result Analysis 493.5.2 Comparative Results Analysis 523.6 Conclusion 53References 544 An Expert System-Based Clinical Decision Support System for Hepatitis-B Prediction & Diagnosis 57Niranjan Panigrahi, Ishan Ayus and Om Prakash Jena4.1 Introduction 574.2 Outline of Clinical DSS 594.2.1 Preliminaries 594.2.2 Types of Clinical DSS 604.2.3 Non-Knowledge-Based Decision Support System (NK-DSS) 604.2.4 Knowledge-Based Decision Support System (K-DSS) 624.2.5 Hybrid Decision Support System (H-DSS) 644.2.6 DSS Architecture 644.3 Background 654.4 Proposed Expert System-Based CDSS 654.4.1 Problem Description 654.4.2 Rules Set & Knowledge Base 664.4.3 Inference Engine 664.5 Implementation & Testing 664.6 Conclusion 73References 735 Deep Learning on Symptoms in Disease Prediction 77Sheikh Raul Islam, Rohit Sinha, Santi P. Maity and Ajoy Kumar Ray5.1 Introduction 775.2 Literature Review 785.3 Mathematical Models 795.3.1 Graphs and Related Terms 805.3.2 Deep Learning in Graph 805.3.3 Network Embedding 805.3.4 Graph Neural Network 815.3.5 Graph Convolution Network 825.4 Learning Representation From DSN 825.4.1 Description of the Proposed Model 835.4.2 Objective Function 845.5 Results and Discussion 845.5.1 Description of the Dataset 855.5.2 Training Progress 855.5.3 Performance Comparisons 865.6 Conclusions and Future Scope 86References 876 Intelligent Vision-Based Systems for Public Safety and Protection via Machine Learning Techniques 89Rajitha B.6.1 Introduction 896.1.1 Problems Intended in Video Surveillance Systems 906.1.2 Current Developments in This Area 916.1.3 Role of AI in Video Surveillance Systems 916.2 Public Safety and Video Surveillance Systems 926.2.1 Offline Crime Prevention 926.2.2 Crime Prevention and Identification via Apps 926.2.3 Crime Prevention and Identification via CCTV 926.3 Machine Learning for Public Safety 946.3.1 Abnormality Behavior Detection via Deep Learning 956.3.2 Video Analytics Methods for Accident Classification/Detection 976.3.3 Feature Selection and Fusion Methods 986.4 Securing the CCTV Data 996.4.1 Image/Video Security Challenges 996.4.2 Blockchain for Image/Video Security 996.5 Conclusion 99References 1007 Semantic Framework in Healthcare 103Sankar Pariserum Perumal, Ganapathy Sannasi, Selvi M. and Kannan Arputharaj7.1 Introduction 1037.2 Semantic Web Ontology 1047.3 Multi-Agent System in a Semantic Framework 1067.3.1 Existing Healthcare Semantic Frameworks 1077.3.1.1 AOIS 1077.3.1.2 SCKE 1087.3.1.3 MASE 1097.3.1.4 MET4 1107.3.2 Proposed Multi-Agent-Based Semantic Framework for Healthcare Instance Data 1117.3.2.1 Data Dictionary 1117.3.2.2 Mapping Database 1127.3.2.3 Decision Making Ontology 1137.3.2.4 STTL and SPARQL-Based RDF Transformation 1157.3.2.5 Query Optimizer Agent 1167.3.2.6 Semantic Web Services Ontology 1167.3.2.7 Web Application User Interface and Customer Agent 1167.3.2.8 Translation Agent 1177.3.2.9 RDF Translator 1177.4 Conclusion 118References 1198 Detection, Prediction & Intervention of Attention Deficiency in the Brain Using tDCS 121Pallabjyoti Kakoti, Rissnalin Syiemlieh and Eeshankur Saikia8.1 Introduction 1218.2 Materials & Methods 1238.2.1 Subjects and Experimental Design 1238.2.2 Data Pre-Processing & Statistical Analysis 1258.2.3 Extracting Singularity Spectrum from EEG 1268.3 Results & Discussion 1268.4 Conclusion 132Acknowledgement 133References 1339 Detection of Onset and Progression of Osteoporosis Using Machine Learning 137Shilpi Ruchi Kerketta and Debalina Ghosh9.1 Introduction 1379.1.1 Measurement Techniques of BMD 1389.1.2 Machine Learning Algorithms in Healthcare 1389.1.3 Organization of Chapter 1399.2 Microwave Characterization of Human Osseous Tissue 1399.2.1 Frequency-Domain Analysis of Human Wrist Sample 1409.2.2 Data Collection and Analysis 1419.3 Prediction Model of Osteoporosis Using Machine Learning Algorithms 1449.3.1 K-Nearest Neighbor (KNN) 1449.3.2 Decision Tree 1459.3.3 Random Forest 1459.4 Conclusion 148Acknowledgment 148References 14810 Applications of Machine Learning in Biomedical Text Processing and Food Industry 151K. Paramesha, Gururaj H.L. and Om Prakash Jena10.1 Introduction 15210.2 Use Cases of AI and ML in Healthcare 15310.2.1 Speech Recognition (SR) 15310.2.2 Pharmacovigilance and Adverse Drug Effects (ADE) 15310.2.3 Clinical Imaging and Diagnostics 15310.2.4 Conversational AI in Healthcare 15410.3 Use Cases of AI and ML in Food Technology 15410.3.1 Assortment of Vegetables and Fruits 15410.3.2 Personal Hygiene 15410.3.3 Developing New Products 15510.3.4 Plant Leaf Disease Detection 15610.3.5 Face Recognition Systems for Domestic Cattle 15610.3.6 Cleaning Processing Equipment 15710.4 A Case Study: Sentiment Analysis of Drug Reviews 15810.4.1 Dataset 15910.4.2 Approaches for Sentiment Analysis on Drug Reviews 15910.4.3 BoW and TF-IDF Model 16010.4.4 Bi-LSTM Model 16010.4.4.1 Word Embedding 16010.4.5 Deep Learning Model 16110.5 Results and Analysis 16410.6 Conclusion 165References 16611 Comparison of MobileNet and ResNet CNN Architectures in the CNN-Based Skin Cancer Classifier Model 169Subasish Mohapatra, N.V.S. Abhishek, Dibyajit Bardhan, Anisha Ankita Ghosh and Shubhadarshinin Mohanty11.1 Introduction 16911.2 Our Skin Cancer Classifier Model 17111.3 Skin Cancer Classifier Model Results 17211.4 Hyperparameter Tuning and Performance 17411.4.1 Hyperparameter Tuning of MobileNet-Based CNN Model 17511.4.2 Hyperparameter Tuning of ResNet50-Based CNN Model 17511.4.3 Table Summary of Hyperparameter Tuning Results 17611.5 Comparative Analysis and Results 17611.5.1 Training and Validation Loss 17711.5.1.1 MobileNet 17711.5.1.2 ResNet50 17711.5.1.3 Inferences 17711.5.2 Training and Validation Categorical Accuracy 17811.5.2.1 MobileNet 17811.5.2.2 ResNet50 17811.5.2.3 Inferences 17811.5.3 Training and Validation Top 2 Accuracy 17911.5.3.1 MobileNet 17911.5.3.2 ResNet50 17911.5.3.3 Inferences 18011.5.4 Training and Validation Top 3 Accuracy 18011.5.4.1 MobileNet 18011.5.4.2 ResNet50 18011.5.4.3 Inferences 18111.5.5 Confusion Matrix 18111.5.5.1 MobileNet 18111.5.5.2 ResNet50 18111.5.5.3 Inferences 18211.5.6 Classification Report 18211.5.6.1 MobileNet 18211.5.6.2 ResNet50 18211.5.6.3 Inferences 18311.5.7 Last Epoch Results 18311.5.7.1 MobileNet 18311.5.7.2 ResNet50 18311.5.7.3 Inferences 18411.5.8 Best Epoch Results 18411.5.8.1 MobileNet 18411.5.8.2 ResNet50 18411.5.8.3 Inferences 18411.5.9 Overall Comparative Analysis 18411.6 Conclusion 185References 18512 Deep Learning-Based Image Classifier for Malaria Cell Detection 187Alok Negi, Krishan Kumar and Prachi Chauhan12.1 Introduction 18712.2 Related Work 18912.3 Proposed Work 19012.3.1 Dataset Description 19112.3.2 Data Pre-Processing and Augmentation 19112.3.3 CNN Architecture and Implementation 19212.4 Results and Evaluation 19412.5 Conclusion 196References 19713 Prediction of Chest Diseases Using Transfer Learning 199S. Baghavathi Priya, M. Rajamanogaran and S. Subha13.1 Introduction 19913.2 Types of Diseases 20013.2.1 Pneumothorax 20013.2.2 Pneumonia 20013.2.3 Effusion 20013.2.4 Atelectasis 20113.2.5 Nodule and Mass 20213.2.6 Cardiomegaly 20213.2.7 Edema 20213.2.8 Lung Consolidation 20213.2.9 Pleural Thickening 20213.2.10 Infiltration 20213.2.11 Fibrosis 20313.2.12 Emphysema 20313.3 Diagnosis of Lung Diseases 20413.4 Materials and Methods 20413.4.1 Data Augmentation 20613.4.2 CNN Architecture 20613.4.3 Lung Disease Prediction Model 20713.5 Results and Discussions 20813.5.1 Implementation Results Using ROC Curve 20913.6 Conclusion 210References 21214 Early Stage Detection of Leukemia Using Artificial Intelligence 215Neha Agarwal and Piyush Agrawal14.1 Introduction 21514.1.1 Classification of Leukemia 21614.1.1.1 Acute Lymphocytic Leukemia 21614.1.1.2 Acute Myeloid Leukemia 21614.1.1.3 Chronic Lymphocytic Leukemia 21614.1.1.4 Chronic Myeloid Leukemia 21614.1.2 Diagnosis of Leukemia 21614.1.3 Acute and Chronic Stages of Leukemia 21714.1.4 The Role of AI in Leukemia Detection 21714.2 Literature Review 21914.3 Proposed Work 22014.3.1 Modules Involved in Proposed Methodology 22114.3.2 Flowchart 22214.3.3 Proposed Algorithm 22314.4 Conclusion and Future Aspects 223References 223Part 3: Internet of Medical Things (IoMT) for Healthcare 22515 IoT Application in Interconnected Hospitals 227Subhra Debdas, Chinmoy Kumar Panigrahi, Priyasmita Kundu, Sayantan Kundu and Ramanand Jha15.1 Introduction 22815.2 Networking Systems Using IoT 22915.3 What are Smart Hospitals? 23315.3.1 Environment of a Smart Hospital 23415.4 Assets 23615.4.1 Overview of Smart Hospital Assets 23615.4.2 Exigency of Automated Healthcare Center Assets 23915.5 Threats 24115.5.1 Emerging Vulnerabilities 24115.5.2 Threat Analysis 24415.6 Conclusion 246References 24616 Real Time Health Monitoring Using IoT With Integration of Machine Learning Approach 249K.G. Maheswari, G. Nalinipriya, C. Siva and A. Thilakesh Raj16.1 Introduction 25016.2 Related Work 25016.3 Existing Healthcare Monitoring System 25116.4 Methodology and Data Analysis 25116.5 Proposed System Architecture 25216.6 Machine Learning Approach 25216.6.1 Multiple Linear Regression Algorithm 25316.6.2 Random Forest Algorithm 25316.6.3 Support Vector Machine 25316.7 Work Flow of the Proposed System 25316.8 System Design of Health Monitoring System 25616.9 Use Case Diagram 25716.10 Conclusion 258References 259Part 4: Machine Learning Applications for COVID-19 26117 Semantic and NLP-Based Retrieval From Covid-19 Ontology 263Ramar Kaladevi and Appavoo Revathi17.1 Introduction 26317.2 Related Work 26417.3 Proposed Retrieval System 26617.3.1 Why Ontology? 26617.3.2 Covid Ontology 26617.3.3 Information Retrieval From Ontology 26917.3.4 Query Formulation 27217.3.5 Retrieval From Knowledgebase 27217.4 Conclusion 273References 27318 Semantic Behavior Analysis of COVID-19 Patients: A Collaborative Framework 277Amlan Mohanty, Debasish Kumar Mallick, Shantipriya Parida and Satya Ranjan Dash18.1 Introduction 27818.2 Related Work 28018.2.1 Semantic Analysis and Topic Discovery of Alcoholic Patients From Social Media Platforms 28018.2.2 Sentiment Analysis of Tweets From Twitter Handles of the People of Nepal in Response to the COVID-19 Pandemic 28018.2.3 Study of Sentiment Analysis and Analyzing Scientific Papers 28018.2.4 Informatics and COVID-19 Research 28118.2.5 COVID-19 Outbreak in the World and Twitter Sentiment Analysis 28118.2.6 LDA Topic Modeling on Twitter to Study Public Discourse and Sentiment During the Coronavirus Pandemic 28118.2.7 The First Decade of Research on Sentiment Analysis 28218.2.8 Detailed Survey on the Semantic Analysis Techniques for NLP 28218.2.9 Understanding Text Semantics With LSA 28218.2.10 Analyzing Suicidal Tendencies With Semantic Analysis Using Social Media 28318.2.11 Analyzing Public Opinion on BREXIT Using Sentiment Analysis 28318.2.12 Prediction of Indian Elections Using NLP and Decision Tree 28318.3 Methodology 28318.4 Conclusion 286References 28719 Comparative Study of Various Data Mining Techniques Towards Analysis and Prediction of Global COVID-19 Dataset 289Sachin Kamley19.1 Introduction 28919.2 Literature Review 29019.3 Materials and Methods 29219.3.1 Dataset Collection 29219.3.2 Support Vector Machine (SVM) 29219.3.3 Decision Tree (DT) 29419.3.4 K-Means Clustering 29419.3.5 Back Propagation Neural Network (BPNN) 29519.4 Experimental Results 29619.5 Conclusion and Future Scopes 305References 30620 Automated Diagnosis of COVID-19 Using Reinforced Lung Segmentation and Classification Model 309J. Shiny Duela and T. Illakiya20.1 Introduction 30920.2 Diagnosis of COVID-19 31020.2.1 Pre-Processing of Lung CT Image 31020.2.2 Lung CT Image Segmentation 31120.2.3 ROI Extraction 31120.2.4 Feature Extraction 31120.2.5 Classification 31120.3 Genetic Algorithm (GA) 31120.3.1 Operators of GA 31220.3.2 Applications of GA 31220.4 Related Works 31320.5 Challenges in GA 31420.6 Challenges in Lung CT Segmentation 31420.7 Proposed Diagnosis Framework 31420.7.1 Image Pre-Processing 31520.7.2 Proposed Image Segmentation Technique 31520.7.3 ROI Segmentation 31820.7.4 Feature Extraction 31820.7.5 Modified GA Classifier 31820.7.5.1 Gaussian Type—II Fuzzy in Classification 31820.7.5.2 Classifier Algorithm 31920.8 Result Discussion 31920.9 Conclusion 321References 321Part 5: Case Studies of Application Areas of Machine Learning in Healthcare System 32321 Future of Telemedicine with ML: Building a Telemedicine Framework for Lung Sound Detection 325Sudhansu Shekhar Patra, Nitin S. Goje, Kamakhya Narain Singh, Kaish Q. Khan, Deepak Kumar, Madhavi and Kumar Ashutosh Sharma21.1 Introduction 32521.1.1 Monitoring the Remote Patient 32621.1.2 Intelligent Assistance for Patient Diagnosis 32621.1.3 Fasten Electronic Health Record Retrieval Process 32621.1.4 Collaboration Increases Among Healthcare Practitioners 32621.2 Related Work 32721.3 Strategic Model for Telemedicine 32821.4 Framework for Lung Sound Detection in Telemedicine 33021.4.1 Data Collection 33021.4.2 Pre-Processing of Data 33121.4.3 Feature Extraction 33121.4.3.1 MFCC 33121.4.3.2 Lung Sounds Using Multi Resolution DWT 33221.4.4 Classification 33421.4.4.1 Correlation Coefficient for Feature Selection (CFS) 33421.4.4.2 Symmetrical Uncertainty 33421.4.4.3 Gain Ratio 33521.4.4.4 Modified RF Classification Architecture 33521.5 Experimental Analysis 33521.6 Conclusion 340References 34022 A Lightweight Convolutional Neural Network Model for Tuberculosis Bacilli Detection From Microscopic Sputum Smear Images 343Rani Oomman Panicker, S.J. Pawan, Jeny Rajan and M.K. Sabu22.1 Introduction 34322.2 Literature Review 34522.3 Proposed Work 34622.4 Experimental Results and Discussion 34922.5 Conclusion 350References 35023 Role of Machine Learning and Texture Features for the Diagnosis of Laryngeal Cancer 353Vibhav Prakash Singh and Ashish Kumar Maurya23.1 Introduction 35323.2 Clinically Correlated Texture Features 35823.2.1 Texture-Based LBP Descriptors 35823.2.2 GLCM Features 35823.2.3 Statistical Features 35923.3 Machine Learning Techniques 35923.3.1 Support Vector Machine (SVM) 35923.3.2 k-NN (k-Nearest Neighbors) 36023.3.3 Random Forest (RF) 36123.3.4 Naïve Bayes 36123.4 Result Analysis and Discussions 36123.5 Conclusions 366References 36624 Analysis of Machine Learning Technologies for the Detection of Diabetic Retinopathy 369Biswabijayee Chandra Sekhar Mohanty, Sonali Mishra and Sambit Kumar Mishra24.1 Introduction 36924.2 Related Work 37024.2.1 Pre-Processing of Image 37124.2.2 Diabetic Retinopathy Detection 37224.2.3 Grading of DR 37424.3 Dataset Used 37424.3.1 DIARETDB1 37424.3.2 Diabetic-Retinopathy-Detection Dataset 37624.4 Methodology Used 37724.4.1 Pre-Processing 37724.4.2 Segmentation 37724.4.3 Feature Extraction 37824.4.4 Classification 37824.5 Analysis of Results and Discussion 37924.6 Conclusion 380References 381Index 383