In den 21 Kapiteln dieses Buches werden verschiedene Aspekte der Computerintelligenz wie maschinelles Lernen und Deep Learning aus unterschiedlichen Perspektiven betrachtet. Mit dem Werk sollen die Fachleute verschiedener Bereiche einen Überblick über die innovativen Fortschritte erhalten, die in der Theorie, bei analytischen Ansätzen, numerischer Simulation, statistischer Analyse, Modellierung, fortschrittlicher Anwendung, Fallstudien, analytischen Ergebnissen und computergestützter Strukturierung gemacht wurden sowie über den beträchtlichem Fortschritt bei Anwendungen im Gesundheitswesen.
In den 21 Kapiteln dieses Buches werden verschiedene Aspekte der Computerintelligenz wie maschinelles Lernen und Deep Learning aus unterschiedlichen Perspektiven betrachtet. Mit dem Werk sollen die Fachleute verschiedener Bereiche einen Überblick über die innovativen Fortschritte erhalten, die in der Theorie, bei analytischen Ansätzen, numerischer Simulation, statistischer Analyse, Modellierung, fortschrittlicher Anwendung, Fallstudien, analytischen Ergebnissen und computergestützter Strukturierung gemacht wurden sowie über den beträchtlichem Fortschritt bei Anwendungen im Gesundheitswesen.
Produktdetails
Produktdetails
Machine Learning in Biomedical Science and Healthcare Informatics
Om Prakash Jena PhD is an assistant professor in the Department of Computer Science, Ravenshaw University, Cuttack, Odisha, India. He has more than 30 research articles in peer-reviewed journals and 4 patents. Alok Ranjan Tripathy PhD is an assistant professor in the Department of Computer Science, Ravenshaw University, Cuttack, Odisha, India. Ahmed A. Elngar PhD is an assistant professor of Computer Science, Chair of Scientific Innovation Research Group (SIRG), Director of Technological and Informatics Studies Center, at Beni-Suef University, Egypt. Zdzislaw Polkowski PhD is Professor in the Faculty of Technical Sciences, Jan Wyzykowski University, Polkowice, Poland. He has published more than 75 research articles in peer-reviewed journals.
Inhaltsangabe
Preface xv
Part I: Introduction 1
1 Machine Learning and Big Data: An Approach Toward Better Healthcare Services 3 Nahid Sami and Asfia Aziz
1.1 Introduction 3
1.2 Machine Learning in Healthcare 4
1.3 Machine Learning Algorithms 6
1.3.1 Supervised Learning 6
1.3.2 Unsupervised Learning 7
1.3.3 Semi-Supervised Learning 7
1.3.4 Reinforcement Learning 8
1.3.5 Deep Learning 8
1.4 Big Data in Healthcare 8
1.5 Application of Big Data in Healthcare 9
1.5.1 Electronic Health Records 9
1.5.2 Helping in Diagnostics 9
1.5.3 Preventive Medicine 10
1.5.4 Precision Medicine 10
1.5.5 Medical Research 10
1.5.6 Cost Reduction 10
1.5.7 Population Health 10
1.5.8 Telemedicine 10
1.5.9 Equipment Maintenance 11
1.5.10 Improved Operational Efficiency 11
1.5.11 Outbreak Prediction 11
1.6 Challenges for Big Data 11
1.7 Conclusion 11
References 12
Part II: Medical Data Processing and Analysis 15
2 Thoracic Image Analysis Using Deep Learning 17 Rakhi Wajgi, Jitendra V. Tembhurne and Dipak Wajgi
2.1 Introduction 18
2.2 Broad Overview of Research 19
2.2.1 Challenges 19
2.2.2 Performance Measuring Parameters 21
2.2.3 Availability of Datasets 21
2.3 Existing Models 23
2.4 Comparison of Existing Models 30
2.5 Summary 38
2.6 Conclusion and Future Scope 38
References 39
3 Feature Selection and Machine Learning Models for High-Dimensional Data: State-of-the-Art 43 G. Manikandan and S. Abirami
3.1 Introduction 43
3.1.1 Motivation of the Dimensionality Reduction 45
3.1.2 Feature Selection and Feature Extraction 46
3.1.3 Objectives of the Feature Selection 47
3.1.4 Feature Selection Process 47
3.2 Types of Feature Selection 48
3.2.1 Filter Methods 49
3.2.1.1 Correlation-Based Feature Selection 49
3.2.1.2 The Fast Correlation-Based Filter 50
3.2.1.3 The INTERACT Algorithm 51
3.2.1.4 ReliefF 51
3.2.1.5 Minimum Redundancy Maximum Relevance 52
3.2.2 Wrapper Methods 52
3.2.3 Embedded Methods 53
3.2.4 Hybrid Methods 54
3.3 Machine Learning and Deep Learning Models 55
3.3.1 Restricted Boltzmann Machine 55
3.3.2 Autoencoder 56
3.3.3 Convolutional Neural Networks 57
3.3.4 Recurrent Neural Network 58
3.4 Real-World Applications and Scenario of Feature Selection 58
3.4.1 Microarray 58
3.4.2 Intrusion Detection 59
3.4.3 Text Categorization 59
3.5 Conclusion 59
References 60
4 A Smart Web Application for Symptom-Based Disease Detection and Prediction Using State-of-the-Art ML and ANN Models 65 Parvej Reja Saleh and Eeshankur Saikia
4.1 Introduction 65
4.2 Literature Review 68
4.3 Dataset, EDA, and Data Processing 69
4.4 Machine Learning Algorithms 72
4.4.1 Multinomial Naïve Bayes Classifier 72
4.4.2 Support Vector Machine Classifier 72
4.4.3 Random Forest Classifier 73
4.4.4 K-Nearest Neighbor Classifier 74
4.4.5 Decision Tree Classifier 74
4.4.6 Logistic Regression Classifier 75
4.4.7 Multilayer Perceptron Classifier 76
4.5 Work Architecture 77
4.6 Conclusion 78
References 79
5 Classification of Heart Sound Signals Using Time-Freq
1 Machine Learning and Big Data: An Approach Toward Better Healthcare Services 3 Nahid Sami and Asfia Aziz
1.1 Introduction 3
1.2 Machine Learning in Healthcare 4
1.3 Machine Learning Algorithms 6
1.3.1 Supervised Learning 6
1.3.2 Unsupervised Learning 7
1.3.3 Semi-Supervised Learning 7
1.3.4 Reinforcement Learning 8
1.3.5 Deep Learning 8
1.4 Big Data in Healthcare 8
1.5 Application of Big Data in Healthcare 9
1.5.1 Electronic Health Records 9
1.5.2 Helping in Diagnostics 9
1.5.3 Preventive Medicine 10
1.5.4 Precision Medicine 10
1.5.5 Medical Research 10
1.5.6 Cost Reduction 10
1.5.7 Population Health 10
1.5.8 Telemedicine 10
1.5.9 Equipment Maintenance 11
1.5.10 Improved Operational Efficiency 11
1.5.11 Outbreak Prediction 11
1.6 Challenges for Big Data 11
1.7 Conclusion 11
References 12
Part II: Medical Data Processing and Analysis 15
2 Thoracic Image Analysis Using Deep Learning 17 Rakhi Wajgi, Jitendra V. Tembhurne and Dipak Wajgi
2.1 Introduction 18
2.2 Broad Overview of Research 19
2.2.1 Challenges 19
2.2.2 Performance Measuring Parameters 21
2.2.3 Availability of Datasets 21
2.3 Existing Models 23
2.4 Comparison of Existing Models 30
2.5 Summary 38
2.6 Conclusion and Future Scope 38
References 39
3 Feature Selection and Machine Learning Models for High-Dimensional Data: State-of-the-Art 43 G. Manikandan and S. Abirami
3.1 Introduction 43
3.1.1 Motivation of the Dimensionality Reduction 45
3.1.2 Feature Selection and Feature Extraction 46
3.1.3 Objectives of the Feature Selection 47
3.1.4 Feature Selection Process 47
3.2 Types of Feature Selection 48
3.2.1 Filter Methods 49
3.2.1.1 Correlation-Based Feature Selection 49
3.2.1.2 The Fast Correlation-Based Filter 50
3.2.1.3 The INTERACT Algorithm 51
3.2.1.4 ReliefF 51
3.2.1.5 Minimum Redundancy Maximum Relevance 52
3.2.2 Wrapper Methods 52
3.2.3 Embedded Methods 53
3.2.4 Hybrid Methods 54
3.3 Machine Learning and Deep Learning Models 55
3.3.1 Restricted Boltzmann Machine 55
3.3.2 Autoencoder 56
3.3.3 Convolutional Neural Networks 57
3.3.4 Recurrent Neural Network 58
3.4 Real-World Applications and Scenario of Feature Selection 58
3.4.1 Microarray 58
3.4.2 Intrusion Detection 59
3.4.3 Text Categorization 59
3.5 Conclusion 59
References 60
4 A Smart Web Application for Symptom-Based Disease Detection and Prediction Using State-of-the-Art ML and ANN Models 65 Parvej Reja Saleh and Eeshankur Saikia
4.1 Introduction 65
4.2 Literature Review 68
4.3 Dataset, EDA, and Data Processing 69
4.4 Machine Learning Algorithms 72
4.4.1 Multinomial Naïve Bayes Classifier 72
4.4.2 Support Vector Machine Classifier 72
4.4.3 Random Forest Classifier 73
4.4.4 K-Nearest Neighbor Classifier 74
4.4.5 Decision Tree Classifier 74
4.4.6 Logistic Regression Classifier 75
4.4.7 Multilayer Perceptron Classifier 76
4.5 Work Architecture 77
4.6 Conclusion 78
References 79
5 Classification of Heart Sound Signals Using Time-Freq
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