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.
1. Chapter 1 : Introduction to predictive analytics and MATLAB® - p. Page xv 2. Chapter 2 : Prognostic insights: predictive analytics in nephrological diseases - p. Page 29 3. Chapter 3 : Harnessing predictive analytics for cardiovascular diseases - p. Page 57 4. Chapter 4 : Predictive analytics in breast cancer prognosis - p. Page 101 5. Chapter 5 : Predicting Parkinson’s: analyzing patterns with data and analytics - p. Page 151 6. Chapter 6 : Predictive analytics for diabetes mellitus: illuminating glucose horizons - p. Page 183 7. Chapter 7 : From data to diagnosis: predictive analytics in liver ailments - p. Page 234 8. Chapter 8 : Predictive analytics in Alzheimer’s disease: pioneering memory projection - p. Page 267 9. Chapter 9 : Prostate cancer prognostication: insights from predictive analytics - p. Page 297 10. Chapter 10 : Leveraging predictive analytics for asthma management - p. Page 332 11. Chapter 11 : Predictive analytics for brain tumor detection and prognosis - p. Page 358 12. Chapter 12 : A comprehensive overview of predictive analytics in biomedical applications - p. Page 419 13. Index - p. Page 445
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.
1. Chapter 1 : Introduction to predictive analytics and MATLAB® - p. Page xv 2. Chapter 2 : Prognostic insights: predictive analytics in nephrological diseases - p. Page 29 3. Chapter 3 : Harnessing predictive analytics for cardiovascular diseases - p. Page 57 4. Chapter 4 : Predictive analytics in breast cancer prognosis - p. Page 101 5. Chapter 5 : Predicting Parkinson’s: analyzing patterns with data and analytics - p. Page 151 6. Chapter 6 : Predictive analytics for diabetes mellitus: illuminating glucose horizons - p. Page 183 7. Chapter 7 : From data to diagnosis: predictive analytics in liver ailments - p. Page 234 8. Chapter 8 : Predictive analytics in Alzheimer’s disease: pioneering memory projection - p. Page 267 9. Chapter 9 : Prostate cancer prognostication: insights from predictive analytics - p. Page 297 10. Chapter 10 : Leveraging predictive analytics for asthma management - p. Page 332 11. Chapter 11 : Predictive analytics for brain tumor detection and prognosis - p. Page 358 12. Chapter 12 : A comprehensive overview of predictive analytics in biomedical applications - p. Page 419 13. Index - p. Page 445
