"Exploratory data analysis helps to recognize natural patterns hidden in the data. This book describes the tools for hypothesis generation by visualizing data through graphical representation and provides insight into advanced analytics concepts in an easy way. The book addresses the complete data visualization technologies workflow, explores basic and high-level concepts of computer science and engineering in medical science, and provides an overview of the clinical scientific research areas that enables smart diagnosis equipment. It will discuss techniques and tools used to explore large volumes of medical data and offers case studies that focus on the innovative technological upgradation and challenges faced today. The primary audience for the book includes specialists, researchers, graduates, designers, experts, physicians, and engineers who are doing research in this domain"--
Exploratory data analysis helps to recognize natural patterns hidden in the data. This book describes the tools for hypothesis generation by visualizing data through graphical representation and provides insight into advanced analytics concepts in an easy way. The book addresses the complete data visualization technologies workflow, explores basic and high-level concepts of computer science and engineering in medical science, and provides an overview of the clinical scientific research areas that enables smart diagnosis equipment. It will discuss techniques and tools used to explore large volumes of medical data and offers case studies that focus on the innovative technological upgradation and challenges faced today.The primary audience for the book includes specialists, researchers, graduates, designers, experts, physicians, and engineers who are doing research in this domain.
Exploratory data analysis helps to recognize natural patterns hidden in the data. This book describes the tools for hypothesis generation by visualizing data through graphical representation and provides insight into advanced analytics concepts in an easy way.
Chapter
1. Visual Analytics: Scopes & Challenges.
Chapter
2. Statistical
Methods and Applications: A Comprehensive Reference for the Healthcare
Industry.
Chapter
3. Machine Learning Algorithms for Healthcare Data
Analytics.
Chapter
4. A Review of Challenges and Opportunities in Machine
Learning for Healthcare.
Chapter
5. Digitalizing the Health Records Using
Machine Learning Algorithms.
Chapter
6. Interactive Visualization for
Understanding and Analyzing Medical Data.
Chapter
7. Heart Disease Prediction
Using Tableau.
Chapter
8. A Deep Learning Framework Using AlexNet for Early
Detection of Pancreatic Cancer.
Chapter
9. Applications of the Map-Reduce
Programming Framework to Clinical Big Data Analysis: Current Landscape and
Future Trends.
Chapter
10. An Investigation of Different Machine Learning
Approaches for Healthcare Analytics.
Chapter
11. The Potential of Machine
Learning for Clinical Predictive Analytics.
Chapter
12. Predictive Analytics
in Healthcare Using Machine Learning Tools and Techniques.
Chapter
13. A
Collective Study of Machine Learning (ML) Algorithms and Its Impact on
Various Facets of Healthcare.
Dr. R. Lakshmana Kumar is an Assistant professor in the Computer Applications Department and currently also leading the technical training team in Hindusthan College of Engineering and Technology, Coimbatore. Tamil Nadu. His PhD is from Anna University, Chennai and his Research is on Semantic Web Services. Part of his PhD work was funded by South Korea. He is a global chapter lead for MLCS [ Machine Learning for Cyber Security] for the Coimbatore chapter. He is currently allied with company-specific training of Infosys Campus Connect, Oracle WDP and Palo Alto Networks. He has a passion for software development and holds an international certification on SCJP (Sun Certificated Java Programmer) and SCJWCD (Sun Certificate Java Web Component Developer). He is familiar with programming languages like Java, Python, and PHP. He is involved with research and considered an expertise in distributed computing. He also holds the Data Science certification from John Hopkins University and the Amazon Cloud Architect certification from Amazon Web Services. He has published more than 25 papers in various international journals.
Dr. R. Indrakumari is an Assistant Professor as the School of Computing Science and Engineering, Galgotias University, NCR Delhi, India. She has completed the M.Tech in Computer and Information Technology from Manonmaniam Sundaranar University, Tirunelveli. Her main areas of interest are Big Data, Internet of Things, Data Mining, Data warehousing and its visualization tools such as Tableau, Qlikview.
Dr. B. Balamurugan Completed his PhD. at Vellore Institute of Technology University, Vellore and is currently working as a Professor at Galgotias University, Greater Noida, Uttar Pradesh. He has 15 years of teaching experience in the field of computer science. His area of interest lies in the field of Internet of Things, Big data, Networking. He has published more than 100 international journals papers and contributed book chapters.
Dr. Achyut Shankar completed his PhD at Vellore Institute of Technology University, Tamilnadu, India and is currently working as an Assistant Professor at Amity School of Engineering and Technology, India. His areas of interested are Data Communication, Computer Networks, Machine Learning, Statistical Tools, Operating Systems, Pattern Recognition, and Theory of Computation.
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