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El. knyga: Cognitive and Soft Computing Techniques for the Analysis of Healthcare Data

Edited by , Edited by (Directorate of Research, Gangtok, Sikkim Manipal University, Sikkim, India), Edited by (Amrita Scho), Edited by (Professor and Senior Researcher, Federal University of Ceara, Fortaleza, Graduate Program on Teleinformatics Engineering, Fortaleza/CE, Brazil)
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Cognitive and Soft Computing Techniques for the Analysis of Healthcare Data discusses the insight of data processing applications in various domains through soft computing techniques and enormous advancements in the field.

The book focuses on the cross-disciplinary mechanisms and ground-breaking research ideas on novel techniques and data processing approaches in handling structured and unstructured healthcare data. It also gives insight into various information-processing models and many memories associated with it while processing the information for forecasting future trends and decision making.

This book is an excellent resource for researchers and professionals who work in the Healthcare Industry, Data Science, and Machine learning.

  • Focuses on data-centric operations in the Healthcare industry
  • Provides the latest trends in healthcare data analytics and practical implementation outcomes of the proposed models
  • Addresses real-time challenges and case studies in the Healthcare industry
Contributors xiii
Preface xvii
Chapter 1 Artificial intelligence and machine learning for the healthcare sector: performing predictions and metrics evaluation of ML classifiers on a diabetic diseases data set
1(28)
Pratiyush Guleria
Manu Sood
1 Introduction
1(4)
1.1 Artificial intelligence and health care
2(2)
1.2 Opportunities and challenges of AI and ML in the healthcare sector
4(1)
2 Smart healthcare system
5(1)
3 Machine learning example of data analytics in health care
6(7)
3.1 Data classification
7(4)
3.2 Clustering techniques
11(2)
4 Experimental results
13(6)
5 Conclusion
19(10)
Abbreviations
20(6)
References
26(3)
Chapter 2 Cognitive technology for a personalized seizure predictive and healthcare analytic device
29(30)
Vishalteja Kosana
Kiran Teeparthi
Abu ul Hassan S. Rana
1 Introduction
29(1)
2 Epilepsy and seizures
30(2)
2.1 Quick statistics
30(1)
2.2 Types of seizures
31(1)
3 Cognitive technology
32(2)
4 Internet of Things
34(2)
4.1 Why do we need the IoT?
34(1)
4.2 Working principle
35(1)
5 Cognitive IoT and neural networks
36(4)
5.1 Deep neural networks
36(3)
5.2 Steps to construct an artificial neural network or deep neural network
39(1)
6 Natural language processing
40(1)
6.1 Classical models
40(1)
6.2 Deep learning models
40(1)
6.3 Applications of natural language processing
40(1)
7 Problem statement
40(1)
8 Methodology
41(4)
9 Proposed approach
45(5)
9.1 Working of the system
48(1)
9.2 Device for different types of seizures
48(1)
9.3 Device for health monitoring
48(2)
10 Simulations and discussions
50(6)
11 Conclusions
56(3)
References
56(3)
Chapter 3 Cognitive Internet of Things (IoT) and computational intelligence for mental well-being
59(20)
Surendrabikram Thapa
Awishkar Ghimire
Surabhi Adhikari
Akash Kumar Bhoi
Paolo Barsocchi
1 Introduction
59(2)
2 Cognitive IoT and computational intelligence in health care
61(1)
3 Computer vision for early diagnosis of mental disorders using MRI
62(2)
4 Feature selection techniques and optimization techniques used
64(2)
5 Natural language processing-based diagnostic system
66(3)
6 Harnessing the power of NLP for the analysis of social media content for depression detection
69(1)
7 Computational intelligence and cognitive IoT in suicide prevention
69(2)
8 Wearables and IoT devices for mental well-being
71(2)
9 Future scope of computational intelligence in mental well-being
73(1)
10 Conclusion
74(5)
References
74(5)
Chapter 4 Artificial neural network-based approaches for computer-aided disease diagnosis and treatment
79(22)
Joao Alexandre Lobo Marques
Francisco Nauber Bernardo Gois
Joao Paulo do Vale Madeiro
Tengyue Li
Simon James Fong
1 Introduction
79(2)
1.1 Structure of this chapter
80(1)
2 Artificial neural networks applied to computer-aided diagnosis and treatment
81(6)
2.1 Computer-aided diagnosis and treatment systems (CADTS)
81(2)
2.2 Artificial neural networks in medical diagnosis
83(1)
2.3 ANN: types and applications
84(3)
2.4 Future trends in ANN for disease diagnosis and treatment
87(1)
3 Application of ANN in the diagnosis and treatment of cardiovascular diseases
87(4)
3.1 Applications in cardiology
88(3)
4 Case study: ANN and medical imaging---brain tumor detection
91(6)
4.1 Proposed methodology
92(3)
4.2 Model analysis
95(1)
4.3 Experiments and results
96(1)
5 Final considerations
97(4)
References
97(4)
Chapter 5 Al and deep learning for processing the huge amount of patient-centric data that assist in clinical decisions
101(22)
Joao Alexandre Labo Marques
Francisco Nauber Bernardo Gois
Jarbas Aryel Nunes da Silveira
Tengyue Li
Simon James Fong
1 Introduction
101(2)
1.1 Structure of this chapter
102(1)
2 Challenges and trends
103(7)
2.1 Clinical decision support systems (CDSSs)
103(1)
2.2 Artificial intelligence x knowledge base systems
103(2)
2.3 The adoption of CDSSs
105(1)
2.4 The challenge of the increasing amounts of data available for clinical decision-making
106(2)
2.5 Artificial intelligence and deep learning for CDSS
108(1)
2.6 Engineers and medical researchers
108(1)
2.7 Developments and trends in big data and AI for CDSS
109(1)
3 Case study 1: multiple Internet of Things (IoT) monitoring systems and deep learning classification systems to support ambulatory maternal---fetal clinical decisions
110(4)
3.1 Preliminary CDSS
111(1)
3.2 Deep learning classifier
112(1)
3.3 Conclusion
113(1)
4 Case study 2: artificial intelligence epidemiology prediction system during the COVID-19 pandemic to assist in clinical decisions
114(5)
4.1 Long-term short-term memory networks (LSTM)
114(1)
4.2 Auto machine learning approach (AutoML)
115(1)
4.3 Results and discussion
115(2)
4.4 Conclusion
117(2)
5 Final considerations
119(4)
References
119(4)
Chapter 6 Universal intraensemble method using nonlinear Al techniques for regression modeling of small medical data sets
123(28)
Ivan Izonin
Roman Tkachenko
1 Introduction and problem statement
123(1)
2 Related concepts
124(2)
3 Universal intraensemble method for handling small medical data
126(4)
3.1 Design of the universal intraensemble method
126(2)
3.2 Algorithm 1: support vector regression using nonlinear kernels
128(1)
3.3 Algorithm 2: general regression neural network
128(2)
3.4 Algorithm 3: RBF neural network
130(1)
4 Practical implementation
130(10)
4.1 Data sets for experimental modeling
130(2)
4.2 Performance indicators
132(1)
4.3 Optimal parameters selection
132(1)
4.4 Algorithm 1: support vector regression using nonlinear kernels
132(2)
4.5 Algorithm 2: general regression neural network
134(1)
4.6 Algorithm 3: RBF neural network
135(3)
4.7 Results
138(2)
5 Comparison and discussion
140(4)
5.1 A comparative study of three different algorithms
140(1)
5.2 Comparison with parental regressors
141(3)
6 Conclusion and future work
144(7)
Appendix A
146(2)
References
148(3)
Chapter 7 Comparisons among different stochastic selections of activation layers for convolutional neural networks for health care
151(14)
Loris Nanni
Alessandra Lumini
Stefano Ghidoni
Gianluca Maguolo
1 Introduction
151(1)
2 Literature review
152(1)
3 Activation functions
153(3)
4 Materials and methods
156(2)
5 Results
158(4)
6 Conclusions
162(3)
Acknowledgments
162(1)
References
162(3)
Chapter 8 Natural computing and unsupervised learning methods in smart healthcare data-centric operations
165(26)
Joseph Bamidele Awotunde
Abidemi Emmanuel Adeniyi
Sunday Adeola Ajagbe
Alfonso Gonzalez-Briones
1 Introduction
165(2)
2 Natural computing in the healthcare industry
167(5)
2.1 Computing inspired by nature (ON)
168(2)
2.2 Recent works using NC algorithms in solving various problems in healthcare systems
170(2)
3 Unsupervised learning techniques in healthcare systems
172(5)
3.1 Clustering
173(1)
3.2 Association
173(4)
4 The data-centric operations in healthcare systems
177(3)
4.1 Applications of data-centric intelligence to healthcare systems
178(2)
5 Case study for application of the particle swarm optimization model for the diagnosis of heart disease
180(1)
5.1 The heart disease data set characteristics
181(1)
5.2 Performance evaluation metrics
181(1)
6 Results and discussion
181(3)
7 Conclusion
184(7)
References
184(7)
Chapter 9 Optimized adaptive tree seed Kalman filter for a diabetes recommendation system---bilevel performance improvement strategy for healthcare applications
191(12)
P. Nagaraj
P. Deepalakshmi
Muhammad Fazal Ijaz
1 Introduction
191(1)
2 Literature review
192(1)
3 The proposed AKF-TSA-based insulin recommendation system
193(4)
3.1 Kalman filtering technique
194(1)
3.2 The adaptive Kalman filtering (AKF) technique
195(1)
3.3 Tree seeding optimization algorithm
196(1)
3.4 Recommendation process
197(1)
4 Results and discussion
197(3)
4.1 Data set used
197(1)
4.2 Performance validation
198(2)
5 Conclusion
200(3)
References
201(2)
Chapter 10 Unsupervised deep learning-based disease diagnosis using medical images
203(18)
M. Ganeshkumar
V. Sowmya
E.A. Gopalakrishnan
K.P. Soman
1 Introduction
203(1)
2 Related works
204(3)
3 Methodology
207(4)
3.1 Feature extraction using PCA-Net
207(3)
3.2 Working of principal component analysis (PCA) operation
210(1)
3.3 K-means classifier
210(1)
4 Experiments
211(1)
4.1 Data set
211(1)
4.2 Hyperparameters
212(1)
4.3 Incremental training of K-means
212(1)
5 Evaluation metrics
212(3)
5.1 Accuracy
212(2)
5.2 Precision
214(1)
5.3 Recall/sensitivity
214(1)
5.4 Fl-score
214(1)
5.5 Specificity
214(1)
5.6 Matthews correlation coefficient
214(1)
5.7 Receiver operating characteristic (ROC) curve
215(1)
5.8 Area under the ROC curve (AUC)
215(1)
6 Experimental results and discussions
215(3)
7 Conclusion
218(1)
8 Future work
218(3)
References
218(3)
Chapter 11 Probabilistic approaches for minimizing the healthcare diagnosis cost through data-centric operations
221(18)
Akhilesh Kumar Sharma
Sachit Bhardwaj
Devesh Kumar Srivastava
Nguyen Ha Huy Cuong
Shamik Tiwari
1 Introduction
221(1)
2 Bayesian neural networks
222(1)
3 Markov chain Monte Carlo (MCMC)
222(4)
3.1 Variational inference (VI)
225(1)
3.2 Applications
225(1)
4 Breast cancer prediction using a Bayesian neural network
226(10)
5 Conclusion
236(3)
References
237(2)
Chapter 12 Effects of EEG-sleep irregularities and its behavioral aspects: review and analysis
239(24)
Santosh Satapathy
D. Loganathan
Akash Kumar Bhoi
Paolo Barsocchi
1 Introduction
239(3)
2 Medical background
242(2)
3 Visual scoring procedure
244(1)
4 AI and sleep staging
245(9)
4.1 Analysis of human sleep behavior using sleep variables
249(1)
4.2 Characteristics of biosignals
250(4)
4.3 Non-REM sleep
254(1)
4.4 Sleep diaries and questions
254(1)
5 Sleep patterns and clinical age
254(2)
5.1 Newborns
254(1)
5.2 Young children
255(1)
5.3 Adolescents
255(1)
5.4 Adults
255(1)
5.5 Gender differences
255(1)
5.6 Elderly people
255(1)
6 Case study of an automated sleep staging system
256(1)
6.1 Experimental data
256(1)
6.2 Proposed ensemble learning stacking model
257(1)
6.3 Sleep staging results in the S-EDF database
257(1)
7
Chapter outcome and conclusion
257(6)
References 263(6)
Index 269
Dr. Akash Kumar Bhoi, holds degrees in B.Tech, M.Tech, and Ph.D., and has been contributing to the field of computer science and engineering. He assumed the role of Assistant Professor (Research) at the Department of Computer Science and Engineering, Sikkim Manipal Institute of Technology (SMIT), India, in 2012. In addition to his academic responsibilities, Dr. Bhoi extended his expertise during a research tenure as a Research Associate at the Wireless Networks (WN) Research Laboratory, Institute of Information Science and Technologies, National Research Council (ISTI-CRN) in Pisa, Italy, from January 20, 2021, to January 19, 2022. Dr. Bhoi further serves as the University Ph.D. Course Coordinator for "Research & Publication Ethics (RPE)." He is an active member of professional organizations such as IEEE, ISEIS, and IAENG, and holds associate membership with IEI and UACEE. He plays a significant role as an editorial board member and reviewer for esteemed Indian and international journals and regularly contributes as a reviewer. His research expertise encompasses a wide array of domains, including Biomedical Technologies, the Internet of Things, Computational Intelligence, Antenna technology, and Renewable Energy. Dr. Bhoi has a notable publication record, with multiple papers featured in national and international journals and conferences. Dr. Bhoi has played a pivotal role in the organization of international conferences and workshops, offering his expertise as a key contributor. Currently, he is involved in editing several books in collaboration with international publishers

Victor Hugo C. de Albuquerque [ M17, SM19] is a collaborator Professor and senior researcher at the Graduate Program on Teleinformatics Engineering at the Federal University of Cearį, Brazil, and at the Graduate Program on Telecommunication Engineering, Federal Institute of Education, Science and Technology of Cearį, Fortaleza/CE, Brazil. He has a Ph.D in Mechanical Engineering from the Federal University of Paraķba (UFPB, 2010), an MSc in Teleinformatics Engineering from the Federal University of Cearį (UFC, 2007), and he graduated in Mechatronics Engineering at the Federal Center of Technological Education of Cearį (CEFETCE, 2006). He is a specialist, mainly, in Image Data Science, IoT, Machine/Deep Learning, Pattern Recognition, Robotic. Parvathaneni Naga Srinivasu has earned his Ph.D. degree at GITAM (Deemed to be University) and his areas of research include Biomedical Imaging, Image Enhancement, Image Segmentation, Object Recognition, Image Encryption, Optimization Algorithms, Soft computing, and Natural Language Processing. He is working as an Assistant Professor at the Department of Computer Science and Engineering, GIT, GITAM (Deemed to be University), Visakhapatnam. He is a member of CSI, IAENG, IARA and a regular reviewer for Scopus indexed journals like JCS and IJAIP, Inderscience. He is a guest editor for the special issues and books that are published by reputed publishers like Bentham Science, Springer, and Elsevier. He is a passionate researcher and his articles have been published in national and international journals alongside conferences.

Gonēalo Marques holds a PhD in Computer Science Engineering and is member of the Portuguese Engineering Association (Ordem dos Engenheiros). He is currently working as Assistant Professor lecturing courses on programming, multimedia and database systems. His current research interests include Internet of Things, Enhanced Living Environments, machine learning, e-health, telemedicine, medical and healthcare systems, indoor air quality monitoring and assessment, and wireless sensor networks. He has more than 80 publications in international journals and conferences, is a frequent reviewer of journals and international conferences and is also involved in several edited books projects.
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