Computational intelligence and healthcare informatics. (2021)

• Record Type: Book
• Further Information: Note: Edited by Om Prakash Jena, Alok Ranjan Tripathy, Ahmed A. Elngar, Zdzislaw Polkowski.
• Editors: Jena, Om Prakash; Tripathy, Alok Ranjan; Elngar, Ahmed A; Polkowski, Zdzislaw
• Contents: 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-Frequency Image Texture Features 81; Sujata Vyas, Mukesh D. Patil and Gajanan K. Birajdar 5.1 Introduction 81 5.1.1 Motivation 82 5.2 Related Work 83 5.3 Theoretical Background 84 5.3.1 Pre-Processing Techniques 84 5.3.2 Spectrogram Generation 85 5.3.2 Feature Extraction 88 5.3.4 Feature Selection 90 5.3.5 Support Vector Machine 91 5.4 Proposed Algorithm 91 5.5 Experimental Results 92 5.5.1 Database 92 5.5.2 Evaluation Metrics 94 5.5.3 Confusion Matrix 94 5.5.4 Results and Discussions 94 5.6 Conclusion 96 References 99 6 Improving Multi-Label Classification in Prototype Selection Scenario 103; Himanshu Suyal and Avtar Singh 6.1 Introduction 103 6.2 Related Work 105 6.3 Methodology 106 6.3.1 Experiments and Evaluation 108 6.4 Performance Evaluation 108 6.5 Experiment Data Set 109 6.6 Experiment Results 110 6.7 Conclusion 117 References 117 7 A Machine Learning–Based Intelligent Computational Framework for the Prediction of Diabetes Disease 121; Maqsood Hayat, Yar Muhammad and Muhammad Tahir 7.1 Introduction 121 7.2 Materials and Methods 123 7.2.1 Dataset 123 7.2.2 Proposed Framework for Diabetes System 124 7.2.3 Pre-Processing of Data 124 7.3 Machine Learning Classification Hypotheses 124 7.3.1 K-Nearest Neighbor 124 7.3.2 Decision Tree 125 7.3.3 Random Forest 126 7.3.4 Logistic Regression 126 7.3.5 Naïve Bayes 126 7.3.6 Support Vector Machine 126 7.3.7 Adaptive Boosting 126 7.3.8 Extra-Tree Classifier 127 7.4 Classifier Validation Method 127 7.4.1 K-Fold Cross-Validation Technique 127 7.5 Performance Evaluation Metrics 127 7.6 Results and Discussion 129 7.6.1 Performance of All Classifiers Using 5-Fold CV Method 129 7.6.2 Performance of All Classifiers Using the 7-Fold Cross-Validation Method 131 7.6.3 Performance of All Classifiers Using 10-Fold CV Method 133 7.7 Conclusion 137 References 137 8 Hyperparameter Tuning of Ensemble Classifiers Using Grid Search and Random Search for Prediction of Heart Disease 139; Dhilsath Fathima M. and S. Justin Samuel 8.1 Introduction 140 8.2 Related Work 140 8.3 Proposed Method 142 8.3.1 Dataset Description 143 8.3.2 Ensemble Learners for Classification Modeling 144 8.3.2.1 Bagging Ensemble Learners 145 8.3.2.2 Boosting Ensemble Learner 147 8.3.3 Hyperparameter Tuning of Ensemble Learners 151 8.3.3.1 Grid Search Algorithm 151 8.3.3.2 Random Search Algorithm 152 8.4 Experimental Outcomes and Analyses 153 8.4.1 Characteristics of UCI Heart Disease Dataset 153 8.4.2 Experimental Result of Ensemble Learners and Performance Comparison 154 8.4.3 Analysis of Experimental Result 154 8.5 Conclusion 157 References 157 9 Computational Intelligence and Healthcare Informatics Part III—Recent Development and Advanced Methodologies 159; Sankar Pariserum Perumal, Ganapathy Sannasi, Santhosh Kumar S.V.N. and Kannan Arputharaj 9.1 Introduction: Simulation in Healthcare 160 9.2 Need for a Healthcare Simulation Process 160 9.3 Types of Healthcare Simulations 161 9.4 AI in Healthcare Simulation 163 9.4.1 Machine Learning Models in Healthcare Simulation 163 9.4.1.1 Machine Learning Model for Post-Surgical Risk Prediction 163 9.4.2 Deep Learning Models in Healthcare Simulation 169 9.4.2.1 Bi-LSTM–Based Surgical Participant Prediction Model 170 9.5 Conclusion 174 References 174 10 Wolfram’s Cellular Automata Model in Health Informatics 179; Sutapa Sarkar and Mousumi Saha 10.1 Introduction 179 10.2 Cellular Automata 181 10.3 Application of
• Edition: 1st
• Publisher Details: Hoboken : Wiley-Scrivener
• Publication Date: 2021
• Extent: 1 online resource
• Subjects: 610.28563; Artificial intelligence -- Medical applications; Medical informatics; Computational intelligence
• Languages: English
• ISBNs: 9781119818694
• Related ISBNs: 9781119818687
• Notes: Note: Description based on CIP data; resource not viewed.
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