Exploratory Data Analytics for Healthcare (Innovations in Big Data and Machine Learning)

Kumar, R. Lakshmana (EDT)/ Indrakumari, R. (EDT)/ Balamurugan, B. (EDT)

CRC Press（2024/10発売）

• 製本 Paperback:紙装版/ペーパーバック版／ページ数 292 p.
• 言語 ENG
• 商品コード 9780367506926
• DDC分類 610.285

Full Description

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.

Contents

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.