Details

Computational Intelligence and Healthcare Informatics


Computational Intelligence and Healthcare Informatics


Machine Learning in Biomedical Science and Healthcare Informatics 1. Aufl.

von: Om Prakash Jena, Alok Ranjan Tripathy, Ahmed A. Elngar, Zdzislaw Polkowski

173,99 €

Verlag: Wiley
Format: EPUB
Veröffentl.: 08.09.2021
ISBN/EAN: 9781119818694
Sprache: englisch
Anzahl Seiten: 432

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Beschreibungen

<b>COMPUTATIONAL INTELLIGENCE <i>and</i> HEALTHCARE INFORMATICS</b> <p><b>The book provides the state-of-the-art innovation, research, design, and implements methodological and algorithmic solutions to data processing problems, designing and analysing evolving trends in health informatics, intelligent disease prediction, and computer-aided diagnosis. </b> <p>Computational intelligence (CI) refers to the ability of computers to accomplish tasks that are normally completed by intelligent beings such as humans and animals. With the rapid advance of technology, artificial intelligence (AI) techniques are being effectively used in the fields of health to improve the efficiency of treatments, avoid the risk of false diagnoses, make therapeutic decisions, and predict the outcome in many clinical scenarios. Modern health treatments are faced with the challenge of acquiring, analyzing and applying the large amount of knowledge necessary to solve complex problems. Computational intelligence in healthcare mainly uses computer techniques to perform clinical diagnoses and suggest treatments. In the present scenario of computing, CI tools present adaptive mechanisms that permit the understanding of data in difficult and changing environments. The desired results of CI technologies profit medical fields by assembling patients with the same types of diseases or fitness problems so that healthcare facilities can provide effectual treatments. <p>This book starts with the fundamentals of computer intelligence and the techniques and procedures associated with it. Contained in this book are state-of-the-art methods of computational intelligence and other allied techniques used in the healthcare system, as well as advances in different CI methods that will confront the problem of effective data analysis and storage faced by healthcare institutions. The objective of this book is to provide researchers with a platform encompassing state-of-the-art innovations; research and design; implementation of methodological and algorithmic solutions to data processing problems; and the design and analysis of evolving trends in health informatics, intelligent disease prediction and computer-aided diagnosis. <p><b>Audience </b> <p>The book is of interest to artificial intelligence and biomedical scientists, researchers, engineers and students in various settings such as pharmaceutical & biotechnology companies, virtual assistants developing companies, medical imaging & diagnostics centers, wearable device designers, healthcare assistance robot manufacturers, precision medicine testers, hospital management, and researchers working in healthcare system.
<p>Preface xv</p> <p><b>Part I: Introduction 1</b></p> <p><b>1 Machine Learning and Big Data: An Approach Toward Better Healthcare Services 3<br /></b><i>Nahid Sami and Asfia Aziz</i></p> <p>1.1 Introduction 3</p> <p>1.2 Machine Learning in Healthcare 4</p> <p>1.3 Machine Learning Algorithms 6</p> <p>1.3.1 Supervised Learning 6</p> <p>1.3.2 Unsupervised Learning 7</p> <p>1.3.3 Semi-Supervised Learning 7</p> <p>1.3.4 Reinforcement Learning 8</p> <p>1.3.5 Deep Learning 8</p> <p>1.4 Big Data in Healthcare 8</p> <p>1.5 Application of Big Data in Healthcare 9</p> <p>1.5.1 Electronic Health Records 9</p> <p>1.5.2 Helping in Diagnostics 9</p> <p>1.5.3 Preventive Medicine 10</p> <p>1.5.4 Precision Medicine 10</p> <p>1.5.5 Medical Research 10</p> <p>1.5.6 Cost Reduction 10</p> <p>1.5.7 Population Health 10</p> <p>1.5.8 Telemedicine 10</p> <p>1.5.9 Equipment Maintenance 11</p> <p>1.5.10 Improved Operational Efficiency 11</p> <p>1.5.11 Outbreak Prediction 11</p> <p>1.6 Challenges for Big Data 11</p> <p>1.7 Conclusion 11</p> <p>References 12</p> <p><b>Part II: Medical Data Processing and Analysis 15</b></p> <p><b>2 Thoracic Image Analysis Using Deep Learning 17<br /></b><i>Rakhi Wajgi, Jitendra V. Tembhurne and Dipak Wajgi</i></p> <p>2.1 Introduction 18</p> <p>2.2 Broad Overview of Research 19</p> <p>2.2.1 Challenges 19</p> <p>2.2.2 Performance Measuring Parameters 21</p> <p>2.2.3 Availability of Datasets 21</p> <p>2.3 Existing Models 23</p> <p>2.4 Comparison of Existing Models 30</p> <p>2.5 Summary 38</p> <p>2.6 Conclusion and Future Scope 38</p> <p>References 39</p> <p><b>3 Feature Selection and Machine Learning Models for High-Dimensional Data: State-of-the-Art 43<br /></b><i>G. Manikandan and S. Abirami</i></p> <p>3.1 Introduction 43</p> <p>3.1.1 Motivation of the Dimensionality Reduction 45</p> <p>3.1.2 Feature Selection and Feature Extraction 46</p> <p>3.1.3 Objectives of the Feature Selection 47</p> <p>3.1.4 Feature Selection Process 47</p> <p>3.2 Types of Feature Selection 48</p> <p>3.2.1 Filter Methods 49</p> <p>3.2.1.1 Correlation-Based Feature Selection 49</p> <p>3.2.1.2 The Fast Correlation-Based Filter 50</p> <p>3.2.1.3 The INTERACT Algorithm 51</p> <p>3.2.1.4 ReliefF 51</p> <p>3.2.1.5 Minimum Redundancy Maximum Relevance 52</p> <p>3.2.2 Wrapper Methods 52</p> <p>3.2.3 Embedded Methods 53</p> <p>3.2.4 Hybrid Methods 54</p> <p>3.3 Machine Learning and Deep Learning Models 55</p> <p>3.3.1 Restricted Boltzmann Machine 55</p> <p>3.3.2 Autoencoder 56</p> <p>3.3.3 Convolutional Neural Networks 57</p> <p>3.3.4 Recurrent Neural Network 58</p> <p>3.4 Real-World Applications and Scenario of Feature Selection 58</p> <p>3.4.1 Microarray 58</p> <p>3.4.2 Intrusion Detection 59</p> <p>3.4.3 Text Categorization 59</p> <p>3.5 Conclusion 59</p> <p>References 60</p> <p><b>4 A Smart Web Application for Symptom-Based Disease Detection and Prediction Using State-of-the-Art ML and ANN Models 65<br /></b><i>Parvej Reja Saleh and Eeshankur Saikia</i></p> <p>4.1 Introduction 65</p> <p>4.2 Literature Review 68</p> <p>4.3 Dataset, EDA, and Data Processing 69</p> <p>4.4 Machine Learning Algorithms 72</p> <p>4.4.1 Multinomial Naïve Bayes Classifier 72</p> <p>4.4.2 Support Vector Machine Classifier 72</p> <p>4.4.3 Random Forest Classifier 73</p> <p>4.4.4 K-Nearest Neighbor Classifier 74</p> <p>4.4.5 Decision Tree Classifier 74</p> <p>4.4.6 Logistic Regression Classifier 75</p> <p>4.4.7 Multilayer Perceptron Classifier 76</p> <p>4.5 Work Architecture 77</p> <p>4.6 Conclusion 78</p> <p>References 79</p> <p><b>5 Classification of Heart Sound Signals Using Time-Frequency Image Texture Features 81<br /></b><i>Sujata Vyas, Mukesh D. Patil and Gajanan K. Birajdar</i></p> <p>5.1 Introduction 81</p> <p>5.1.1 Motivation 82</p> <p>5.2 Related Work 83</p> <p>5.3 Theoretical Background 84</p> <p>5.3.1 Pre-Processing Techniques 84</p> <p>5.3.2 Spectrogram Generation 85</p> <p>5.3.2 Feature Extraction 88</p> <p>5.3.4 Feature Selection 90</p> <p>5.3.5 Support Vector Machine 91</p> <p>5.4 Proposed Algorithm 91</p> <p>5.5 Experimental Results 92</p> <p>5.5.1 Database 92</p> <p>5.5.2 Evaluation Metrics 94</p> <p>5.5.3 Confusion Matrix 94</p> <p>5.5.4 Results and Discussions 94</p> <p>5.6 Conclusion 96</p> <p>References 99</p> <p><b>6 Improving Multi-Label Classification in Prototype Selection Scenario 103<br /></b><i>Himanshu Suyal and Avtar Singh</i></p> <p>6.1 Introduction 103</p> <p>6.2 Related Work 105</p> <p>6.3 Methodology 106</p> <p>6.3.1 Experiments and Evaluation 108</p> <p>6.4 Performance Evaluation 108</p> <p>6.5 Experiment Data Set 109</p> <p>6.6 Experiment Results 110</p> <p>6.7 Conclusion 117</p> <p>References 117</p> <p><b>7 A Machine Learning–Based Intelligent Computational Framework for the Prediction of Diabetes Disease 121<br /></b><i>Maqsood Hayat, Yar Muhammad and Muhammad Tahir</i></p> <p>7.1 Introduction 121</p> <p>7.2 Materials and Methods 123</p> <p>7.2.1 Dataset 123</p> <p>7.2.2 Proposed Framework for Diabetes System 124</p> <p>7.2.3 Pre-Processing of Data 124</p> <p>7.3 Machine Learning Classification Hypotheses 124</p> <p>7.3.1 K-Nearest Neighbor 124</p> <p>7.3.2 Decision Tree 125</p> <p>7.3.3 Random Forest 126</p> <p>7.3.4 Logistic Regression 126</p> <p>7.3.5 Naïve Bayes 126</p> <p>7.3.6 Support Vector Machine 126</p> <p>7.3.7 Adaptive Boosting 126</p> <p>7.3.8 Extra-Tree Classifier 127</p> <p>7.4 Classifier Validation Method 127</p> <p>7.4.1 K-Fold Cross-Validation Technique 127</p> <p>7.5 Performance Evaluation Metrics 127</p> <p>7.6 Results and Discussion 129</p> <p>7.6.1 Performance of All Classifiers Using 5-Fold CV Method 129</p> <p>7.6.2 Performance of All Classifiers Using the 7-Fold Cross-Validation Method 131</p> <p>7.6.3 Performance of All Classifiers Using 10-Fold CV Method 133</p> <p>7.7 Conclusion 137</p> <p>References 137</p> <p><b>8 Hyperparameter Tuning of Ensemble Classifiers Using Grid Search and Random Search for Prediction of Heart Disease 139<br /></b><i>Dhilsath Fathima M. and S. Justin Samuel</i></p> <p>8.1 Introduction 140</p> <p>8.2 Related Work 140</p> <p>8.3 Proposed Method 142</p> <p>8.3.1 Dataset Description 143</p> <p>8.3.2 Ensemble Learners for Classification Modeling 144</p> <p>8.3.2.1 Bagging Ensemble Learners 145</p> <p>8.3.2.2 Boosting Ensemble Learner 147</p> <p>8.3.3 Hyperparameter Tuning of Ensemble Learners 151</p> <p>8.3.3.1 Grid Search Algorithm 151</p> <p>8.3.3.2 Random Search Algorithm 152</p> <p>8.4 Experimental Outcomes and Analyses 153</p> <p>8.4.1 Characteristics of UCI Heart Disease Dataset 153</p> <p>8.4.2 Experimental Result of Ensemble Learners and Performance Comparison 154</p> <p>8.4.3 Analysis of Experimental Result 154</p> <p>8.5 Conclusion 157</p> <p>References 157</p> <p><b>9 Computational Intelligence and Healthcare Informatics Part III—Recent Development and Advanced Methodologies 159<br /></b><i>Sankar Pariserum Perumal, Ganapathy Sannasi, Santhosh Kumar S.V.N. and Kannan Arputharaj</i></p> <p>9.1 Introduction: Simulation in Healthcare 160</p> <p>9.2 Need for a Healthcare Simulation Process 160</p> <p>9.3 Types of Healthcare Simulations 161</p> <p>9.4 AI in Healthcare Simulation 163</p> <p>9.4.1 Machine Learning Models in Healthcare Simulation 163</p> <p>9.4.1.1 Machine Learning Model for Post-Surgical Risk Prediction 163</p> <p>9.4.2 Deep Learning Models in Healthcare Simulation 169</p> <p>9.4.2.1 Bi-LSTM–Based Surgical Participant Prediction Model 170</p> <p>9.5 Conclusion 174</p> <p>References 174</p> <p><b>10 Wolfram’s Cellular Automata Model in Health Informatics 179<br /></b><i>Sutapa Sarkar and Mousumi Saha</i></p> <p>10.1 Introduction 179</p> <p>10.2 Cellular Automata 181</p> <p>10.3 Application of Cellular Automata in Health Science 183</p> <p>10.4 Cellular Automata in Health Informatics 184</p> <p>10.5 Health Informatics–Deep Learning–Cellular Automata 190</p> <p>10.6 Conclusion 191</p> <p>References 191</p> <p><b>Part III: Machine Learning and COVID Prospective 193</b></p> <p><b>11 COVID-19: Classification of Countries for Analysis and Prediction of Global Novel Corona Virus Infections Disease Using Data Mining Techniques 195<br /></b><i>Sachin Kamley, Shailesh Jaloree, R.S. Thakur and Kapil Saxena</i></p> <p>11.1 Introduction 195</p> <p>11.2 Literature Review 196</p> <p>11.3 Data Pre-Processing 197</p> <p>11.4 Proposed Methodologies 198</p> <p>11.4.1 Simple Linear Regression 198</p> <p>11.4.2 Association Rule Mining 202</p> <p>11.4.3 Back Propagation Neural Network 203</p> <p>11.5 Experimental Results 204</p> <p>11.6 Conclusion and Future Scopes 211</p> <p>References 212</p> <p><b>12 Sentiment Analysis on Social Media for Emotional Prediction During COVID-19 Pandemic Using Efficient Machine Learning Approach 215<br /></b><i>Sivanantham Kalimuthu</i></p> <p>12.1 Introduction 215</p> <p>12.2 Literature Review 218</p> <p>12.3 System Design 222</p> <p>12.3.1 Extracting Feature With WMAR 224</p> <p>12.4 Result and Discussion 229</p> <p>12.5 Conclusion 232</p> <p>References 232</p> <p><b>13 Primary Healthcare Model for Remote Area Using Self-Organizing Map Network 235<br /></b><i>Sayan Das and Jaya Sil</i></p> <p>13.1 Introduction 236</p> <p>13.2 Background Details and Literature Review 239</p> <p>13.2.1 Fuzzy Set 239</p> <p>13.2.2 Self-Organizing Mapping 239</p> <p>13.3 Methodology 240</p> <p>13.3.1 <i>Severity_Factor </i>of Patient 244</p> <p>13.3.2 Clustering by Self-Organizing Mapping 249</p> <p>13.4 Results and Discussion 250</p> <p>13.5 Conclusion 252</p> <p>References 252</p> <p><b>14 Face Mask Detection in Real-Time Video Stream Using Deep Learning 255<br /></b><i>Alok Negi and Krishan Kumar</i></p> <p>14.1 Introduction 256</p> <p>14.2 Related Work 257</p> <p>14.3 Proposed Work 258</p> <p>14.3.1 Dataset Description 258</p> <p>14.3.2 Data Pre-Processing and Augmentation 258</p> <p>14.3.3 VGG19 Architecture and Implementation 259</p> <p>14.3.4 Face Mask Detection From Real-Time Video Stream 261</p> <p>14.4 Results and Evaluation 262</p> <p>14.5 Conclusion 267</p> <p>References 267</p> <p><b>15 A Computational Intelligence Approach for Skin Disease Identification Using Machine/Deep Learning Algorithms 269<br /></b><i>Swathi Jamjala Narayanan, Pranav Raj Jaiswal, Ariyan Chowdhury, Amitha Maria Joseph and Saurabh Ambar</i></p> <p>15.1 Introduction 270</p> <p>15.2 Research Problem Statements 274</p> <p>15.3 Dataset Description 274</p> <p>15.4 Machine Learning Technique Used for Skin Disease Identification 276</p> <p>15.4.1 Logistic Regression 277</p> <p>15.4.1.1 Logistic Regression Assumption 277</p> <p>15.4.1.2 Logistic Sigmoid Function 277</p> <p>15.4.1.3 Cost Function and Gradient Descent 278</p> <p>15.4.2 SVM 279</p> <p>15.4.3 Recurrent Neural Networks 281</p> <p>15.4.4 Decision Tree Classification Algorithm 283</p> <p>15.4.5 CNN 286</p> <p>15.4.6 Random Forest 288</p> <p>15.5 Result and Analysis 290</p> <p>15.6 Conclusion 291</p> <p>References 291</p> <p><b>16 Asymptotic Patients’ Healthcare Monitoring and Identification of Health Ailments in Post COVID-19 Scenario 297<br /></b><i>Pushan K.R. Dutta, Akshay Vinayak and Simran Kumari</i></p> <p>16.1 Introduction 298</p> <p>16.1.1 Motivation 298</p> <p>16.1.2 Contributions 299</p> <p>16.1.3 Paper Organization 299</p> <p>16.1.4 System Model Problem Formulation 299</p> <p>16.1.5 Proposed Methodology 300</p> <p>16.2 Material Properties and Design Specifications 301</p> <p>16.2.1 Hardware Components 301</p> <p>16.2.1.1 Microcontroller 301</p> <p>16.2.1.2 ESP8266 Wi-Fi Shield 301</p> <p>16.2.2 Sensors 301</p> <p>16.2.2.1 Temperature Sensor (LM 35) 301</p> <p>16.2.2.2 ECG Sensor (AD8232) 301</p> <p>16.2.2.3 Pulse Sensor 301</p> <p>16.2.2.4 GPS Module (NEO 6M V2) 302</p> <p>16.2.2.5 Gyroscope (GY-521) 302</p> <p>16.2.3 Software Components 302</p> <p>16.2.3.1 Arduino Software 302</p> <p>16.2.3.2 MySQL Database 302</p> <p>16.2.3.3 Wireless Communication 302</p> <p>16.3 Experimental Methods and Materials 303</p> <p>16.3.1 Simulation Environment 303</p> <p>16.3.1.1 System Hardware 303</p> <p>16.3.1.2 Connection and Circuitry 304</p> <p>16.3.1.3 Protocols Used 306</p> <p>16.3.1.4 Libraries Used 307</p> <p>16.4 Simulation Results 307</p> <p>16.5 Conclusion 310</p> <p>16.6 Abbreviations and Acronyms 310</p> <p>References 311</p> <p><b>17 COVID-19 Detection System Using Cellular Automata–Based Segmentation Techniques 313<br /></b><i>Rupashri Barik, M. Nazma B. J. Naskar and Sarbajyoti Mallik</i></p> <p>17.1 Introduction 313</p> <p>17.2 Literature Survey 314</p> <p>17.2.1 Cellular Automata 315</p> <p>17.2.2 Image Segmentation 316</p> <p>17.2.3 Deep Learning Techniques 316</p> <p>17.3 Proposed Methodology 317</p> <p>17.4 Results and Discussion 320</p> <p>17.5 Conclusion 322</p> <p>References 322</p> <p><b>18 Interesting Patterns From COVID-19 Dataset Using Graph-Based Statistical Analysis for Preventive Measures 325<br /></b><i>Abhilash C. B. and Kavi Mahesh</i></p> <p>18.1 Introduction 326</p> <p>18.2 Methods 326</p> <p>18.2.1 Data 326</p> <p>18.3 GSA Model: Graph-Based Statistical Analysis 327</p> <p>18.4 Graph-Based Analysis 329</p> <p>18.4.1 Modeling Your Data as a Graph 329</p> <p>18.4.2 RDF for Knowledge Graph 331</p> <p>18.4.3 Knowledge Graph Representation 331</p> <p>18.4.4 RDF Triple for KaTrace 333</p> <p>18.4.5 Cipher Query Operation on Knowledge Graph 335</p> <p>18.4.5.1 Inter-District Travel 335</p> <p>18.4.5.2 Patient 653 Spread Analysis 336</p> <p>18.4.5.3 Spread Analysis Using Parent-Child Relationships 337</p> <p>18.4.5.4 Delhi Congregation Attended the Patient’s Analysis 339</p> <p>18.5 Machine Learning Techniques 339</p> <p>18.5.1 Apriori Algorithm 339</p> <p>18.5.2 Decision Tree Classifier 341</p> <p>18.5.3 System Generated Facts on Pandas 343</p> <p>18.5.4 Time Series Model 345</p> <p>18.6 Exploratory Data Analysis 346</p> <p>18.6.1 Statistical Inference 347</p> <p>18.7 Conclusion 356</p> <p>18.8 Limitations 356</p> <p>Acknowledgments 356</p> <p>Abbreviations 357</p> <p>References 357</p> <p><b>Part IV: Prospective of Computational Intelligence in Healthcare 359</b></p> <p><b>19 Conceptualizing Tomorrow’s Healthcare Through Digitization 361<br /></b><i>Riddhi Chatterjee, Ratula Ray, Satya Ranjan Dash and Om Prakash Jena</i></p> <p>19.1 Introduction 361</p> <p>19.2 Importance of IoMT in Healthcare 362</p> <p>19.3 Case Study I: An Integrated Telemedicine Platform in Wake of the COVID-19 Crisis 363</p> <p>19.3.1 Introduction to the Case Study 363</p> <p>19.3.2 Merits 363</p> <p>19.3.3 Proposed Design 363</p> <p>19.3.3.1 Homecare 363</p> <p>19.3.3.2 Healthcare Provider 365</p> <p>19.3.3.3 Community 367</p> <p>19.4 Case Study II: A Smart Sleep Detection System to Track the Sleeping Pattern in Patients Suffering From Sleep Apnea 371</p> <p>19.4.1 Introduction to the Case Study 371</p> <p>19.4.2 Proposed Design 373</p> <p>19.5 Future of Smart Healthcare 375</p> <p>19.6 Conclusion 375</p> <p>References 375</p> <p><b>20 Domain Adaptation of Parts of Speech Annotators in Hindi Biomedical Corpus: An NLP Approach 377<br /></b><i>Pitambar Behera and Om Prakash Jena</i></p> <p>20.1 Introduction 377</p> <p>20.1.1 COVID-19 Pandemic Situation 378</p> <p>20.1.2 Salient Characteristics of Biomedical Corpus 378</p> <p>20.2 Review of Related Literature 379</p> <p>20.2.1 Biomedical NLP Research 379</p> <p>20.2.2 Domain Adaptation 379</p> <p>20.2.3 POS Tagging in Hindi 380</p> <p>20.3 Scope and Objectives 380</p> <p>20.3.1 Research Questions 380</p> <p>20.3.2 Research Problem 380</p> <p>20.3.3 Objectives 381</p> <p>20.4 Methodological Design 381</p> <p>20.4.1 Method of Data Collection 381</p> <p>20.4.2 Method of Data Annotation 381</p> <p>20.4.2.1 The BIS Tagset 381</p> <p>20.4.2.2 ILCI Semi-Automated Annotation Tool 382</p> <p>20.4.2.3 IA Agreement 383</p> <p>20.4.3 Method of Data Analysis 383</p> <p>20.4.3.1 The Theory of Support Vector Machines 384</p> <p>20.4.3.2 Experimental Setup 384</p> <p>20.5 Evaluation 385</p> <p>20.5.1 Error Analysis 386</p> <p>20.5.2 Fleiss’ Kappa 388</p> <p>20.6 Issues 388</p> <p>20.7 Conclusion and Future Work 388</p> <p>Acknowledgements 389</p> <p>References 389</p> <p><b>21 Application of Natural Language Processing in Healthcare 393<br /></b><i>Khushi Roy, Subhra Debdas, Sayantan Kundu, Shalini Chouhan, Shivangi Mohanty and Biswarup Biswas</i></p> <p>21.1 Introduction 393</p> <p>21.2 Evolution of Natural Language Processing 395</p> <p>21.3 Outline of NLP in Medical Management 396</p> <p>21.4 Levels of Natural Language Processing in Healthcare 397</p> <p>21.5 Opportunities and Challenges From a Clinical Perspective 399</p> <p>21.5.1 Application of Natural Language Processing in the Field of Medical Health Records 399</p> <p>21.5.2 Using Natural Language Processing for Large-Sample Clinical Research 400</p> <p>21.6 Openings and Difficulties From a Natural Language Processing Point of View 401</p> <p>21.6.1 Methods for Developing Shareable Data 401</p> <p>21.6.2 Intrinsic Evaluation and Representation Levels 402</p> <p>21.6.3 Beyond Electronic Health Record Data 403</p> <p>21.7 Actionable Guidance and Directions for the Future 403</p> <p>21.8 Conclusion 406</p> <p>References 406</p> <p>Index 409</p>
<p><b> Om Prakash Jena PhD</b> 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. </p> <p><b> Alok Ranjan Tripathy PhD</b> is an assistant professor in the Department of Computer Science, Ravenshaw University, Cuttack, Odisha, India. <p><b> Ahmed A. Elngar PhD</b> 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. <p><b> Zdzislaw Polkowski PhD</b> 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.
<p><b>The book provides the state-of-the-art innovation, research, design, and implements methodological and algorithmic solutions to data processing problems, designing and analysing evolving trends in health informatics, intelligent disease prediction, and computer-aided diagnosis. </b></p> <p>Computational intelligence (CI) refers to the ability of computers to accomplish tasks that are normally completed by intelligent beings such as humans and animals. With the rapid advance of technology, artificial intelligence (AI) techniques are being effectively used in the fields of health to improve the efficiency of treatments, avoid the risk of false diagnoses, make therapeutic decisions, and predict the outcome in many clinical scenarios. Modern health treatments are faced with the challenge of acquiring, analyzing and applying the large amount of knowledge necessary to solve complex problems. Computational intelligence in healthcare mainly uses computer techniques to perform clinical diagnoses and suggest treatments. In the present scenario of computing, CI tools present adaptive mechanisms that permit the understanding of data in difficult and changing environments. The desired results of CI technologies profit medical fields by assembling patients with the same types of diseases or fitness problems so that healthcare facilities can provide effectual treatments. <p>This book starts with the fundamentals of computer intelligence and the techniques and procedures associated with it. Contained in this book are state-of-the-art methods of computational intelligence and other allied techniques used in the healthcare system, as well as advances in different CI methods that will confront the problem of effective data analysis and storage faced by healthcare institutions. The objective of this book is to provide researchers with a platform encompassing state-of-the-art innovations; research and design; implementation of methodological and algorithmic solutions to data processing problems; and the design and analysis of evolving trends in health informatics, intelligent disease prediction and computer-aided diagnosis. <p><b>Audience </b> <p>The book is of interest to artificial intelligence and biomedical scientists, researchers, engineers and students in various settings such as pharmaceutical & biotechnology companies, virtual assistants developing companies, medical imaging & diagnostics centers, wearable device designers, healthcare assistance robot manufacturers, precision medicine testers, hospital management, and researchers working in healthcare system.

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