Predictive Analytics using MATLAB(R) for Biomedical Applications
eBook - PDF

Predictive Analytics using MATLAB(R) for Biomedical Applications

  1. 500 pages
  2. English
  3. PDF
  4. Available on iOS & Android
eBook - PDF

Predictive Analytics using MATLAB(R) for Biomedical Applications

About this book

Predictive Analytics using MATLAB(R) for Biomedical Applications is a comprehensive and practical guide for biomedical engineers, data scientists, and researchers on how to use predictive analytics techniques in MATLAB(R) for solving real-world biomedical problems. The book offers a technical overview of various predictive analytics methods and covers the utilization of MATLAB(R) for implementing these techniques. It includes several case studies that demonstrate how predictive analytics can be applied to real-world biomedical problems, such as predicting disease progression, analyzing medical imaging data, and optimizing treatment outcomes.With a plethora of examples and exercises, this book is the ultimate tool for reinforcing one's knowledge and skills. - Covers various predictive analytics methods, including regression analysis, time series analysis, and machine learning algorithms, providing readers with a comprehensive understanding of the field - Provides a hands-on approach to learning predictive analytics, with a focus on practical applications in biomedical engineering - Includes several case studies that demonstrate the practical application of predictive analytics in real-world biomedical problems, such as disease progression prediction, medical imaging analysis, and treatment optimization

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Yes, you can access Predictive Analytics using MATLAB(R) for Biomedical Applications by L. Ashok Kumar in PDF and/or ePUB format, as well as other popular books in Biological Sciences & Biotechnology. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Front Cover
  2. Predictive Analytics Using MATLABĀ® for Biomedical Applications
  3. Copyright Page
  4. Contents
  5. About the author
  6. Preface
  7. Acknowledgments
  8. About the book
  9. 1 Introduction to predictive analytics and MATLABĀ®
  10. 2 Prognostic insights: predictive analytics in nephrological diseases
  11. 3 Harnessing predictive analytics for cardiovascular diseases