Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics
FIRST EDITION
Pradeep N
Professor, Computer Science and Engineering, Bapuji Institute of Engineering and Technology, Davangere, Karnataka, India
Sandeep Kautish
Professor & Dean Academics, LBEF Campus, Kathmandu, Nepal
(In Academic Collaboration with APUTI Malaysia)
Sheng-Lung Peng
Professor, Department of Computer Science and Information Engineering, National Dong Hwa University, Taiwan
Chapter 1: Foundations of healthcare informatics
Abstract
1.1: Introduction
1.2: Goals of healthcare informatics
1.3: Focus of healthcare informatics
1.4: Applications of healthcare informatics
1.5: Medical information
1.6: Clinical decision support systems
1.7: Developing clinical decision support systems
1.8: Healthcare information management
1.9: Control flow
1.10: Other perspectives
1.11: Conclusion
Chapter 2: Smart healthcare systems using big data
Abstract
2.1: Introduction
2.2: Big data analytics in healthcare
2.3: Related work
2.4: Big data for biomedicine
2.5: Proposed solutions for smart healthcare model
2.6: Role of sensor technology for eHealth
2.7: Major applications and challenges
2.8: Conclusion and future scope
Chapter 3: Big data-based frameworks for healthcare systems
Abstract
3.1: Introduction
3.2: The role of big data in healthcare systems and industry
3.3: Big data frameworks for healthcare systems
3.4: Overview of big data techniques and technologies supporting healthcare systems
3.5: Overview of big data platform and tools for healthcare systems
3.6: Proposed big data-based conceptual framework for healthcare systems
3.7: Conclusion
Chapter 4: Predictive analysis and modeling in healthcare systems
Abstract
4 1: Introduction
4.2: Process configuration and modeling in healthcare systems
5.11: Conclusion
Chapter 6: Emergence of decision support systems in healthcare
Abstract
6.1: Introduction
6.2: Transformation in healthcare systems
6.3: CDS-based technologies
6.4: Clinical data-driven society
6.5: Future of decision support system
6.6: Example: Decision support system
6.7: Conclusion
Section 2: Machine learning and deep learning for healthcare
Chapter 7: A comprehensive review on deep learning techniques for a BCI-based communication system
Abstract Acknowledgments
7.1: Introduction
7.2: Communication system for paralytic people
7.3: Acquisition system
7.4: Machine learning techniques in EEG signal processing
7.5: Deep learning techniques in EEG signal processing
7.6: Performance metrics
7.7: Inferences
7.8: Research challenges and opportunities
7.9: Future scope
7.10: Conclusion
Chapter 8: Clinical diagnostic systems based on machine learning and deep learning
Abstract
8.1: Introduction
8.2: Literature review and discussion
8.3: Applications of machine learning and deep learning in healthcare systems
8.4: Proposed methodology
8 5: Results and discussion
8.6: Future scope and perceptive
8.7: Conclusion
Chapter 9: An improved time-frequency method for efficient diagnosis of cardiac arrhythmias
Abstract
Acknowledgments
11.1: Introduction
11.2: Literature review
11.3: ML workflow
11.4: Experimental setup
11.5: Supervised ML algorithms
11.6: Ensemble ML models
11.7: Results and discussion
11.8: Summary
Chapter 12: Convolutional Siamese networks for one-shot malaria parasite recognition in microscopic images
Abstract
12.1: Introduction
12.2: Related works
12.3: Materials and methods
12.4: Proposed methodology
12.5: Results and discussions
12.6: Conclusions
Chapter 13: Kidney disease prediction using a machine learning approach: A comparative and comprehensive analysis
Abstract
13 1: Introduction
13.2: Machine learning importance in disease prediction
13.3: ML models used in the study
13 4: Results and discussion
13.5: Conclusion
Index
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Dedication
Dedicated to the almighty and our well wishers.
Contributors
Shridhar Allagi Department of Computer Science and Engineering, KLE Institute of Technology, Hubballi, India
J. Anitha Karunya Institute of Technology and Sciences, Coimbatore, Tamilnadu, India
B. Annappa Department of Computer Science and Engineering, National Institute of Technology Karnataka, Mangalore, India
Siddhartha Kumar Arjaria Rajkiya Engineering College, Banda, U ar Pradesh, India
M. Bhuvaneshwari Karunya Institute of Technology and Sciences, Coimbatore, Tamilnadu, India
Chinmay Chakraborty Dept. of Electronics & Communication Engineering, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, India
Jincy S. Cherian The Bhopal School of Social Sciences, Bhopal, Madhya Pradesh, India
S. Thomas George Karunya Institute of Technology and Sciences, Coimbatore, Tamilnadu, India
Yogita Gupta Thapar Institute of Engineering and Technology, Patiala, India
Sanjeevakumar M. Ha ure Basaveshwar Engineering College (Autonomous), Bagalkot, Karnataka, India
Aboobucker Ilmudeen Department of Management and Information Technology, Faculty of Management and Commerce,
Sandeep Raj Department of Electronics and Communication Engineering, Indian Institute of Information Technology Bhagalpur, Bhagalpur, India
Megha Rathi Dept. of Computer Science & Engineering, JIIT, Noida, U ar Pradesh, India
A. Reyana Department of Computer Science and Engineering, Hindusthan College of Engineering and Technology, Coimbatore, Tamilnadu, India
B. Rohit Department of Computer Science, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, Telangana, India
K. Sai Vardhan Department of Information Technology, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, Telangana, India
H.A. Sanjay Department of Information Science and Engineering, Ni e Meenakshi Institute of Technology, Bengaluru, India
S. Sankarananth Excel College of Engineering and Technology, Namakkal, Tamil Nadu, India
B. Sathis Kumar VIT University, Chennai, India
Shravan B.K. Department of Computer Science and Engineering, Visvesvaraya Technological University, Belagavi, India
Kavita Singh Computer Technology, Yeshwantrao Chavan College of Engineering, Nagpur, India
S. Sountharrajan VIT Bhopal University, Bhopal, Madhya Pradesh, India
Likewin Thomas Department of Computer Science and Engineering, PES Institute of Technology and Management,
Shivamogga, India
Editors biography
Dr. Pradeep N is a professor of computer science and engineering at the Bapuji Institute of Engineering and Technology in Davangere, Karnataka, India, affiliated with Visvesvaraya Technological University in Belagavi, Karnataka, India. He has 18 years of academic experience, which includes teaching and research experience. He has worked at various positions, including moving from a lecturer to an associate professor. He has been appointed as a senior member of the Iranian Neuroscience Society–FARS Chapter (SM-FINSS) for a duration of 2 years (March 1, 2021, to March 2, 2023). His research areas of interest include machine learning, pa ern recognition, medical image analysis, knowledge discovery techniques, and data analytics. He is presently guiding two research scholars on knowledge discovery and medical image analysis. He has successfully edited a book published by IGI Publishers, United States. He is also editing books to be published by Elsevier, IGI, DeGruyter, and Scrivener Publishing, which are in progress. He has published more than 20 research articles in refereed journals and also authored six book chapters. He is a reviewer of various international conferences and a few journals, including Multimedia Tools and Applications, Springer. His one Indian patent application has been published and one Australian patent has been granted. He is a professional member in IEEE, ACM, ISTE, and IEI. He was named the Outstanding Teacher in Computer Science and Engineering during the third Global Outreach Research and Education Summit and Awards in 2019, organized by the Global Outreach Research and Education Association. Also, he is a technical commi ee member for Davangere Smart City, Davangere.
p as the University of Kufa, the University of Babylon, the Polytechnic University of the Philippines (PUP), the University of Madras, Anna University Chennai, Savitribai Phule Pune University, M.S. University, Tirunelveli, and various other technical universities.
Google Scholar h ps://scholar.google.co.in/citations? user=O3mUpVQAAAAJ&hl=en.
Linkedin Profile h ps://www.linkedin.com/in/sandeep-k40316b20/.
ORCID Profile h ps://orcid.org/0000-0001-5120-5741.
More details about the academic profile can be found at www.sandeepkautish.com.
Sheng-Lung Peng is a professor and the director of the Department of Creative Technologies and Product Design, National Taipei University of Business, Taiwan. He received his BS degree in mathematics from National Tsing Hua University, and his MS and PhD degrees in computer science from the National Chung Cheng University and the National Tsing Hua University, Taiwan, respectively. He is an honorary professor at the Beijing Information Science and Technology University, China, and a visiting professor at the Ningxia Institute of Science and Technology, China. He is also an adjunct professor at Mandsaur University, India. He serves as the secretary general of the ACM-ICPC Contest Council for Taiwan and the regional director of the ICPC Asia Taipei-Hsinchu site. He is a director of the Institute of Information and Computing Machinery of the Information Service Association of Chinese Colleges and of the Taiwan Association of Cloud Computing. He is also a supervisor of the Chinese Information Literacy Association and the Association of Algorithms and Computation Theory. Dr. Peng has edited several special issues at journals, such as Soft Computing, the Journal of Internet Technology, the Journal of Real-Time Image Processing, the International Journal of Knowledge and System Science, MDPI Algorithms, and so on. He is also a reviewer for more than 10 journals such as IEEE Access and Transactions on Emerging Topics in Computing, IEEE/ACM Transactions on Networking, Theoretical Computer Science, the Journal of Computer and System Sciences, the Journal of Combinatorial Optimization, the Journal of Modelling in
p g Management, Soft Computing, Information Processing Le ers, Discrete Mathematics, Discrete Applied Mathematics, Discussiones Mathematica Graph Theory, and so on. His research interests are in designing and analyzing algorithms for bioinformatics, combinatorics, data mining, and network areas, in which he has published more than 100 research papers.
Foreword
Siddhartha Bha acharyya, Principal, Rajnagar Mahavidyalaya, Birbhum, West Bengal, India
Healthcare has assumed paramount importance in this work-a-day world, thanks to the emergence of life-threatening diseases and ailments. As the name suggests, healthcare analytics is targeted toward evolving technologies and methods to facilitate the measurement, management, and analysis of healthcare data for be er diagnosis and prognosis. As such, healthcare analytics-based interpretations are expected to properly enable medical practitioners to make more effective and efficient operational and clinical decisions. Added to that, the insights gained from proper healthcare data analytics can help one to be er understand the factors behind operational and clinical successes, their medical outcomes, the underlying costs of delivery, optimized healthcare resources and utilities, and other aspects essential for overall improvement.
One of the obvious fallouts of business analytics in the healthcare industry is improving the quality-of-care experience by means of facilitating the most informed decision-making around patient experience by the fruitful analysis of healthcare data.
In this competitive world, healthcare organizations need to uncover consumer care preferences by means of proper analysis. Thus, they require clear knowledge of optimized healthcare delivery. Analytics in the healthcare industry assist healthcare providers in assimilating the knowledge base regarding be er and meaningful expansion, types of specialty services in which to invest, and which current services to optimize.
