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E-raamat: Computational Intelligence and Healthcare Informatics

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Edited by (Ravenshaw University, India), Edited by (Jan Wyzykowski University, Poland), Edited by (Beni-Suef University, Egypt), Edited by (Ravenshaw University, India)
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COMPUTATIONAL INTELLIGENCE and HEALTHCARE INFORMATICS 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.

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.

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.

Audience

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.
Preface xv
Part I Introduction
1(14)
1 Machine Learning and Big Data: An Approach Toward Better Healthcare Services
3(12)
Nahid Sami
Asfia Aziz
1.1 Introduction
3(1)
1.2 Machine Learning in Healthcare
4(2)
1.3 Machine Learning Algorithms
6(2)
1.3.1 Supervised Learning
6(1)
1.3.2 Unsupervised Learning
7(1)
1.3.3 Semi-Supervised Learning
7(1)
1.3.4 Reinforcement Learning
8(1)
1.3.5 Deep Learning
8(1)
1.4 Big Data in Healthcare
8(1)
1.5 Application of Big Data in Healthcare
9(2)
1.5.1 Electronic Health Records
9(1)
1.5.2 Helping in Diagnostics
9(1)
1.5.3 Preventive Medicine
10(1)
1.5.4 Precision Medicine
10(1)
1.5.5 Medical Research
10(1)
1.5.6 Cost Reduction
10(1)
1.5.7 Population Health
10(1)
1.5.8 Telemedicine
10(1)
1.5.9 Equipment Maintenance
11(1)
1.5.10 Improved Operational Efficiency
11(1)
1.5.11 Outbreak Prediction
11(1)
1.6 Challenges for Big Data
11(1)
1.7 Conclusion
11(4)
References
12(3)
Part II Medical Data Processing and Analysis
15(178)
2 Thoracic Image Analysis Using Deep Learning
17(26)
Rakhi Wajgi
Jitendra V. Tembhurne
Dipak Wajgi
2.1 Introduction
18(1)
2.2 Broad Overview of Research
19(4)
2.2.1 Challenges
19(2)
2.2.2 Performance Measuring Parameters
21(1)
2.2.3 Availability of Datasets
21(2)
2.3 Existing Models
23(7)
2.4 Comparison of Existing Models
30(8)
2.5 Summary
38(1)
2.6 Conclusion and Future Scope
38(5)
References
39(4)
3 Feature Selection and Machine Learning Models for High-Dimensional Data: State-of-the-Art
43(22)
G. Manikandan
S. Abirami
3.1 Introduction
43(5)
3.1.1 Motivation of the Dimensionality Reduction
45(1)
3.1.2 Feature Selection and Feature Extraction
46(1)
3.1.3 Objectives of the Feature Selection
47(1)
3.1.4 Feature Selection Process
47(1)
3.2 Types of Feature Selection
48(7)
3.2.1 Filter Methods
49(1)
3.2.1.1 Correlation-Based Feature Selection
49(1)
3.2.1.2 The Fast Correlation-Based Filter
50(1)
3.2.1.3 The INTERACT Algorithm
51(1)
3.2.1.4 ReliefF
51(1)
3.2.1.5 Minimum Redundancy Maximum Relevance
52(1)
3.2.2 Wrapper Methods
52(1)
3.2.3 Embedded Methods
53(1)
3.2.4 Hybrid Methods
54(1)
3.3 Machine Learning and Deep Learning Models
55(3)
3.3.1 Restricted Boltzmann Machine
55(1)
3.3.2 Autoencoder
56(1)
3.3.3 Convolutional Neural Networks
57(1)
3.3.4 Recurrent Neural Network
58(1)
3.4 Real-World Applications and Scenario of Feature Selection
58(1)
3.4.1 Microarray
58(1)
3.4.2 Intrusion Detection
59(1)
3.4.3 Text Categorization
59(1)
3.5 Conclusion
59(6)
References
60(5)
4 A Smart Web Application for Symptom-Based Disease Detection and Prediction Using State-of-the-Art ML and ANN Models
65(16)
Parvej Reja Saleh
Eeshankur Saikia
4.1 Introduction
65(3)
4.2 Literature Review
68(1)
4.3 Dataset, EDA, and Data Processing
69(3)
4.4 Machine Learning Algorithms
72(5)
4.4.1 Multinomial Naive Bayes Classifier
72(1)
4.4.2 Support Vector Machine Classifier
72(1)
4.4.3 Random Forest Classifier
73(1)
4.4.4 K-Nearest Neighbor Classifier
74(1)
4.4.5 Decision Tree Classifier
74(1)
4.4.6 Logistic Regression Classifier
75(1)
4.4.7 Multilayer Perceptron Classifier
76(1)
4.5 Work Architecture
77(1)
4.6 Conclusion
78(3)
References
79(2)
5 Classification of Heart Sound Signals Using Time-Frequency Image Texture Features
81(22)
Sujata Vyas
Mukesh D. Patil
Gajanan K. Birajdar
5.1 Introduction
81(2)
5.1.1 Motivation
82(1)
5.2 Related Work
83(1)
5.3 Theoretical Background
84(7)
5.3.1 Pre-Processing Techniques
84(1)
5.3.2 Spectrogram Generation
85(3)
5.3.2 Feature Extraction
88(2)
5.3.4 Feature Selection
90(1)
5.3.5 Support Vector Machine
91(1)
5.4 Proposed Algorithm
91(1)
5.5 Experimental Results
92(4)
5.5.1 Database
92(2)
5.5.2 Evaluation Metrics
94(1)
5.5.3 Confusion Matrix
94(1)
5.5.4 Results and Discussions
94(2)
5.6 Conclusion
96(7)
References
99(4)
6 Improving Multi-Label Classification in Prototype Selection Scenario
103(18)
Himanshu Suyal
Avtar Singh
6.1 Introduction
103(2)
6.2 Related Work
105(1)
6.3 Methodology
106(2)
6.3.1 Experiments and Evaluation
108(1)
6.4 Performance Evaluation
108(1)
6.5 Experiment Data Set
109(1)
6.6 Experiment Results
110(7)
6.7 Conclusion
117(4)
References
117(4)
7 A Machine Learning-Based Intelligent Computational Framework for the Prediction of Diabetes Disease
121(18)
Maqsood Hayat
Yar Muhammad
Muhammad Tahir
7.1 Introduction
121(2)
7.2 Materials and Methods
123(1)
7.2.1 Dataset
123(1)
7.2.2 Proposed Framework for Diabetes System
124(1)
7.2.3 Pre-Processing of Data
124(1)
7.3 Machine Learning Classification Hypotheses
124(3)
7.3.1 K-Nearest Neighbor
124(1)
7.3.2 Decision Tree
125(1)
7.3.3 Random Forest
126(1)
7.3.4 Logistic Regression
126(1)
7.3.5 Naive Bayes
126(1)
7.3.6 Support Vector Machine
126(1)
7.3.7 Adaptive Boosting
126(1)
7.3.8 Extra-Tree Classifier
127(1)
7.4 Classifier Validation Method
127(1)
7.4.1 K-Fold Cross-Validation Technique
127(1)
7.5 Performance Evaluation Metrics
127(2)
7.6 Results and Discussion
129(8)
7.6.1 Performance of All Classifiers Using 5-Fold CV Method
129(2)
7.6.2 Performance of All Classifiers Using the 7-Fold Cross-Validation Method
131(2)
7.6.3 Performance of All Classifiers Using 10-Fold CV Method
133(4)
7.7 Conclusion
137(2)
References
137(2)
8 Hyperparameter Tuning of Ensemble Classifiers Using Grid Search and Random Search for Prediction of Heart Disease
139(20)
Dhilsath Fathima M.
S. Justin Samuel
8.1 Introduction
140(1)
8.2 Related Work
140(2)
8.3 Proposed Method
142(11)
8.3.1 Dataset Description
143(1)
8.3.2 Ensemble Learners for Classification Modeling
144(1)
8.3.2.1 Bagging Ensemble Learners
145(2)
8.3.2.2 Boosting Ensemble Learner
147(4)
8.3.3 Hyperparameter Tuning of Ensemble Learners
151(1)
8.3.3.1 Grid Search Algorithm
151(1)
8.3.3.2 Random Search Algorithm
152(1)
8.4 Experimental Outcomes and Analyses
153(4)
8.4.1 Characteristics of UCI Heart Disease Dataset
153(1)
8.4.2 Experimental Result of Ensemble Learners and Performance Comparison
154(1)
8.4.3 Analysis of Experimental Result
154(3)
8.5 Conclusion
157(2)
References
157(2)
9 Computational Intelligence and Healthcare Informatics Part III--Recent Development and Advanced Methodologies
159(20)
Sankar Pariserum Perumal
Ganapathy Sannasi
Santhosh Kumar
Kannan Arputharaj
9.1 Introduction: Simulation in Healthcare
160(1)
9.2 Need for a Healthcare Simulation Process
160(1)
9.3 Types of Healthcare Simulations
161(2)
9.4 AI in Healthcare Simulation
163(11)
9.4.1 Machine Learning Models in Healthcare Simulation
163(1)
9.4.1.1 Machine Learning Model for Post-Surgical Risk Prediction
163(6)
9.4.2 Deep Learning Models in Healthcare Simulation
169(1)
9.4.2.1 Bi-LSTM-Based Surgical Participant Prediction Model
170(4)
9.5 Conclusion
174(5)
References
174(5)
10 Wolfram's Cellular Automata Model in Health Informatics
179(14)
Sutapa Sarkar
Mousumi Saha
10.1 Introduction
179(2)
10.2 Cellular Automata
181(2)
10.3 Application of Cellular Automata in Health Science
183(1)
10.4 Cellular Automata in Health Informatics
184(6)
10.5 Health Informatics-Deep Learning-Cellular Automata
190(1)
10.6 Conclusion
191(2)
References
191(2)
Part III Machine Learning and COVID Prospective
193(166)
11 COVID-19: Classification of Countries for Analysis and Prediction of Global Novel Corona Virus Infections Disease Using Data Mining Techniques
195(20)
Sachin Kamley
Shailesh Jaloree
R.S. Thakur
Kapil Saxena
11.1 Introduction
195(1)
11.2 Literature Review
196(1)
11.3 Data Pre-Processing
197(1)
11.4 Proposed Methodologies
198(6)
11.4.1 Simple Linear Regression
198(4)
11.4.2 Association Rule Mining
202(1)
11.4.3 Back Propagation Neural Network
203(1)
11.5 Experimental Results
204(7)
11.6 Conclusion and Future Scopes
211(4)
References
212(3)
12 Sentiment Analysis on Social Media for Emotional Prediction During COVID-19 Pandemic Using Efficient Machine Learning Approach
215(20)
Sivananthatn Kalimuthu
12.1 Introduction
215(3)
12.2 Literature Review
218(4)
12.3 System Design
222(7)
12.3.1 Extracting Feature With WMAR
224(5)
12.4 Result and Discussion
229(3)
12.5 Conclusion
232(3)
References
232(3)
13 Primary Healthcare Model for Remote Area Using Self-Organizing Map Network
235(20)
Sayan Das
Jaya Sil
13.1 Introduction
236(3)
13.2 Background Details and Literature Review
239(1)
13.2.1 Fuzzy Set
239(1)
13.2.2 Self-Organizing Mapping
239(1)
13.3 Methodology
240(10)
13.3.1 Severity Factor of Patient
244(5)
13.3.2 Clustering by Self-Organizing Mapping
249(1)
13.4 Results and Discussion
250(2)
13.5 Conclusion
252(3)
References
252(3)
14 Face Mask Detection in Real-Time Video Stream Using Deep Learning
255(14)
Alok Negi
Krishan Kumar
14.1 Introduction
256(1)
14.2 Related Work
257(1)
14.3 Proposed Work
258(4)
14.3.1 Dataset Description
258(1)
14.3.2 Data Pre-Processing and Augmentation
258(1)
14.3.3 VGG19 Architecture and Implementation
259(2)
14.3.4 Face Mask Detection From Real-Time Video Stream
261(1)
14.4 Results and Evaluation
262(5)
14.5 Conclusion
267(2)
References
267(2)
15 A Computational Intelligence Approach for Skin Disease Identification Using Machine/Deep Learning Algorithms
269(28)
Swathi Jamjala Narayanan
Pranav Raj Jaiswal
Ariyan Chowdhury
Amitha Maria Joseph
Saurabh Ambar
15.1 Introduction
270(4)
15.2 Research Problem Statements
274(1)
15.3 Dataset Description
274(2)
15.4 Machine Learning Technique Used for Skin Disease Identification
276(14)
15.4.1 Logistic Regression
277(1)
15.4.1.1 Logistic Regression Assumption
277(1)
15.4.1.2 Logistic Sigmoid Function
277(1)
15.4.1.3 Cost Function and Gradient Descent
278(1)
15.4.2 SVM
279(2)
15.4.3 Recurrent Neural Networks
281(2)
15.4.4 Decision Tree Classification Algorithm
283(3)
15.4.5 CNN
286(2)
15.4.6 Random Forest
288(2)
15.5 Result and Analysis
290(1)
15.6 Conclusion
291(6)
References
291(6)
16 Asymptotic Patients' Healthcare Monitoring and Identification of Health Ailments in Post COVID-19 Scenario
297(16)
Pushan K.R. Dutta
Akshay Vinayak
Simran Kumari
16.1 Introduction
298(3)
16.1.1 Motivation
298(1)
16.1.2 Contributions
299(1)
16.1.3 Paper Organization
299(1)
16.1.4 System Model Problem Formulation
299(1)
16.1.5 Proposed Methodology
300(1)
16.2 Material Properties and Design Specifications
301(2)
16.2.1 Hardware Components
301(1)
16.2.1.1 Microcontroller
301(1)
16.2.1.2 ESP8266 Wi-Fi Shield
301(1)
16.2.2 Sensors
301(1)
16.2.2.1 Temperature Sensor (LM 35)
301(1)
16.2.2.2 ECG Sensor (AD8232)
301(1)
16.2.2.3 Pulse Sensor
301(1)
16.2.2.4 GPS Module (NEO 6M V2)
302(1)
16.2.2.5 Gyroscope (GY-521)
302(1)
16.2.3 Software Components
302(1)
16.2.3.1 Arduino Software
302(1)
16.2.3.2 MySQL Database
302(1)
16.2.3.3 Wireless Communication
302(1)
16.3 Experimental Methods and Materials
303(4)
16.3.1 Simulation Environment
303(1)
16.3.1.1 System Hardware
303(1)
16.3.1.2 Connection and Circuitry
304(2)
16.3.1.3 Protocols Used
306(1)
16.3.1.4 Libraries Used
307(1)
16.4 Simulation Results
307(3)
16.5 Conclusion
310(1)
16.6 Abbreviations and Acronyms
310(3)
References
311(2)
17 COVID-19 Detection System Using Cellular Automata-Based Segmentation Techniques
313(12)
Rupashri Barik
M. Nazma
B. J. Naskar
Sarbajyoti Mallik
17.1 Introduction
313(1)
17.2 Literature Survey
314(3)
17.2.1 Cellular Automata
315(1)
17.2.2 Image Segmentation
316(1)
17.2.3 Deep Learning Techniques
316(1)
17.3 Proposed Methodology
317(3)
17.4 Results and Discussion
320(2)
17.5 Conclusion
322(3)
References
322(3)
18 Interesting Patterns From COVID-19 Dataset Using Graph-Based Statistical Analysis for Preventive Measures
325(34)
Abhilash C. B.
Kavi Mahesh
18.1 Introduction
326(1)
18.2 Methods
326(1)
18.2.1 Data
326(1)
18.3 GSA Model: Graph-Based Statistical Analysis
327(2)
18.4 Graph-Based Analysis
329(10)
18.4.1 Modeling Your Data as a Graph
329(2)
18.4.2 RDF for Knowledge Graph
331(1)
18.4.3 Knowledge Graph Representation
331(2)
18.4.4 RDF Triple for KaTrace
333(2)
18.4.5 Cipher Query Operation on Knowledge Graph
335(1)
18.4.5.1 Inter-District Travel
335(1)
18.4.5.2 Patient
335(1)
Spread Analysis
336(1)
18.4.5.3 Spread Analysis Using Parent-Child Relationships
337(2)
18.4.5.4 Delhi Congregation Attended the Patient's Analysis
339(1)
18.5 Machine Learning Techniques
339(7)
18.5.1 Apriori Algorithm
339(2)
18.5.2 Decision Tree Classifier
341(2)
18.5.3 System Generated Facts on Pandas
343(2)
18.5.4 Time Series Model
345(1)
18.6 Exploratory Data Analysis
346(10)
18.6.1 Statistical Inference
347(9)
18.7 Conclusion
356(1)
18.8 Limitations
356(3)
Acknowledgments
356(1)
Abbreviations
357(1)
References
357(2)
Part IV Prospective of Computational Intelligence in Healthcare
359(47)
19 Conceptualizing Tomorrow's Healthcare Through Digitization
361(16)
Riddhi Chatterjee
Ratula Ray
Satya Ranjan Dash
Om Prakash Jena
19.1 Introduction
361(1)
19.2 Importance of IoMT in Healthcare
362(1)
19.3 Case Study I: An Integrated Telemedicine Platform in Wake oftheCOVID-19 Crisis
363(8)
19.3.1 Introduction to the Case Study
363(1)
19.3.2 Merits
363(1)
19.3.3 Proposed Design
363(1)
19.3.3.1 Homecare
363(2)
19.3.3.2 Healthcare Provider
365(2)
19.3.3.3 Community
367(4)
19.4 Case Study II: A Smart Sleep Detection System to Track the Sleeping Pattern in Patients Suffering From Sleep Apnea
371(4)
19.4.1 Introduction to the Case Study
371(2)
19.4.2 Proposed Design
373(2)
19.5 Future of Smart Healthcare
375(1)
19.6 Conclusion
375(2)
References
375(2)
20 Domain Adaptation of Parts of Speech Annotators in Hindi Biomedical Corpus: An NLP Approach
377(16)
Pitamhar Behera
Om Prakash Jena
20.1 Introduction
377(2)
20.1.1 COVID-19 Pandemic Situation
378(1)
20.1.2 Salient Characteristics of Biomedical Corpus
378(1)
20.2 Review of Related Literature
379(1)
20.2.1 Biomedical NLP Research
379(1)
20.2.2 Domain Adaptation
379(1)
20.2.3 POS Tagging in Hindi
380(1)
20.3 Scope and Objectives
380(1)
20.3.1 Research Questions
380(1)
20.3.2 Research Problem
380(1)
20.3.3 Objectives
381(1)
20.4 Methodological Design
381(4)
20.4.1 Method of Data Collection
381(1)
20.4.2 Method of Data Annotation
381(1)
20.4.2.1 The BIS Tagset
381(1)
20.4.2.2 ILCI Semi-Automated Annotation Tool
382(1)
20.4.2.3 IA Agreement
383(1)
20.4.3 Method of Data Analysis
383(1)
20.4.3.1 The Theory of Support Vector Machines
384(1)
20.4.3.2 Experimental Setup
384(1)
20.5 Evaluation
385(3)
20.5.1 Error Analysis
386(2)
20.5.2 Fleiss' Kappa
388(1)
20.6 Issues
388(1)
20.7 Conclusion and Future Work
388(5)
Acknowledgements
389(1)
References
389(4)
21 Application of Natural Language Processing in Healthcare
393(13)
Khushi Roy
Subhra Debdas
Sayantan Kundu
Shalini Chouhan
Shivangi Mohanty
Biswarup Biswas
21.1 Introduction
393(2)
21.2 Evolution of Natural Language Processing
395(1)
21.3 Outline of NLP in Medical Management
396(1)
21.4 Levels of Natural Language Processing in Healthcare
397(2)
21.5 Opportunities and Challenges From a Clinical Perspective
399(2)
21.5.1 Application of Natural Language Processing in the Field of Medical Health Records
399(1)
21.5.2 Using Natural Language Processing for Large-Sample Clinical Research
400(1)
21.6 Openings and Difficulties From a Natural Language Processing Point of View
401(2)
21.6.1 Methods for Developing Shareable Data
401(1)
21.6.2 Intrinsic Evaluation and Representation Levels
402(1)
21.6.3 Beyond Electronic Health Record Data
403(1)
21.7 Actionable Guidance and Directions for the Future
403(3)
21.8 Conclusion
406(1)
References 406(3)
Index 409
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.
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