Predictive Analytics using MATLAB(R) for Biomedical Applications
- 1st Edition - September 26, 2024
- Imprint: Academic Press
- Author: L. Ashok Kumar
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 2 9 8 8 8 - 2
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 2 9 8 8 9 - 9
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 pr… Read more
Purchase options
Institutional subscription on ScienceDirect
Request a sales quotePredictive 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.
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
Biomedical engineers, Biomedical researchers, Biomedical engineering Graduate and Post Graduate Students and Professors
- Cover image
- Title page
- Table of Contents
- Copyright
- About the author
- Preface
- Acknowledgments
- About the book
- Chapter 1. Introduction to predictive analytics and MATLAB®
- Abstract
- 1.1 Introduction
- 1.2 Foundations of biomedical data
- 1.3 Characteristics and challenges of processing biomedical signals
- 1.4 Introduction to predictive analytics
- 1.5 Predictive analytics in healthcare
- 1.6 Predictive analytics techniques
- 1.7 Machine learning algorithms
- 1.8 Introduction to MATLAB
- 1.9 Mathematical modeling in biomedical engineering
- 1.10 Conclusion
- References
- Chapter 2. Prognostic insights: predictive analytics in nephrological diseases
- Abstract
- 2.1 Introduction
- 2.2 Kidney disease
- 2.3 Need for kidney disease prediction
- 2.4 Techniques for kidney disease assessment
- 2.5 Publicly available dataset
- 2.6 Related work
- 2.7 Experimental analysis using MATLAB®
- 2.8 Results and discussion
- 2.9 Conclusion
- References
- Chapter 3. Harnessing predictive analytics for cardiovascular diseases
- Abstract
- 3.1 Introduction
- 3.2 Open access datasets
- 3.3 Related work
- 3.4 K-means clustering
- 3.5 Decision tree
- 3.6 Conclusion
- References
- Chapter 4. Predictive analytics in breast cancer prognosis
- Abstract
- 4.1 Introduction
- 4.2 Literature survey
- 4.3 Dataset description and preprocessing
- 4.4 Machine learning algorithms for breast cancer prediction
- 4.5 Pretrained convolutional neural network models for the prediction of breast cancer
- 4.6 Results and discussion
- 4.7 Conclusion
- References
- Chapter 5. Predicting Parkinson’s: analyzing patterns with data and analytics
- Abstract
- 5.1 Introduction
- 5.2 Components of Parkinson’s
- 5.3 Ratio of Parkinson’s over the years
- 5.4 Need for early prediction of Parkinson’s
- 5.5 Techniques for Parkinson’s assessment
- 5.6 Publicly available dataset
- 5.7 Related work
- 5.8 Experimental analysis using MATLAB®
- 5.9 Methodology
- 5.10 MATLAB code appendix
- 5.11 Results and discussion
- 5.12 Conclusion
- References
- Chapter 6. Predictive analytics for diabetes mellitus: illuminating glucose horizons
- Abstract
- 6.1 Introduction
- 6.2 Related works
- 6.3 Data preparation
- 6.4 Naive Bayes
- 6.5 Deep neural network
- 6.6 Conclusion
- References
- Chapter 7. From data to diagnosis: predictive analytics in liver ailments
- Abstract
- 7.1 Introduction
- 7.2 Liver disease
- 7.3 Characteristics of liver disease
- 7.4 Ratio of liver disease over the years
- 7.5 Need for liver disease prediction
- 7.6 Techniques for liver disease assessment
- 7.7 Publicly available dataset
- 7.8 Related work
- 7.9 Experimental analysis using MATLAB
- 7.10 Dataset description
- 7.11 Methodology
- 7.12 Result and discussion
- 7.13 Conclusion
- References
- Chapter 8. Predictive analytics in Alzheimer’s disease: pioneering memory projection
- Abstract
- 8.1 Introduction
- 8.2 Alzheimer’s disease origin
- 8.3 Related works
- 8.4 Open access datasets
- 8.5 Experimental analysis using MATLAB®
- 8.6 Methodology
- 8.7 MATLAB code appendix
- 8.8 Code description
- 8.9 Results and discussion
- 8.10 Conclusion
- References
- Chapter 9. Prostate cancer prognostication: insights from predictive analytics
- Abstract
- 9.1 Introduction
- 9.2 Literature review
- 9.3 Data types in prostate cancer research
- 9.4 Data collection and preprocessing
- 9.5 MATLAB in prostate cancer prediction
- 9.6 Regression
- 9.7 Evaluation metrics
- 9.8 MATLAB implementation
- 9.9 Train regression models in regression learner app
- 9.10 Discussion
- 9.11 Conclusion
- References
- Chapter 10. Leveraging predictive analytics for asthma management
- Abstract
- 10.1 Introduction
- 10.2 Asthma disease
- 10.3 Types
- 10.4 Characteristics of asthma
- 10.5 Need for asthma disease prediction
- 10.6 Techniques for asthma disease assessment
- 10.7 Publicly available datasets
- 10.8 Related work
- 10.9 Experimental analysis using MATLAB
- 10.10 Dataset description
- 10.11 Statistical analysis on dataset
- 10.12 Methodology
- 10.13 Results and discussion
- 10.14 Conclusion
- References
- Chapter 11. Predictive analytics for brain tumor detection and prognosis
- Abstract
- 11.1 Introduction
- 11.2 Related works
- 11.3 Data collection and preprocessing
- 11.4 Convolutional neural networks
- 11.5 Naïve Bayes
- 11.6 Conclusion
- References
- Chapter 12. A comprehensive overview of predictive analytics in biomedical applications
- Abstract
- 12.1 Introduction
- 12.2 Objectives and scope
- 12.3 Methodology overview
- 12.4 Key findings
- 12.5 Model training in MATLAB
- 12.6 Machine learning model
- 12.7 Feature importance and selection
- 12.8 Disease prediction accuracy
- 12.9 Discussion of results
- 12.10 Addressing model limitations
- 12.11 Practical implications
- 12.12 Early disease detection benefits
- 12.13 Challenges and future directions
- 12.14 Ethical considerations
- 12.15 Conclusion and significance
- Index
- Edition: 1
- Published: September 26, 2024
- Imprint: Academic Press
- No. of pages: 500
- Language: English
- Paperback ISBN: 9780443298882
- eBook ISBN: 9780443298899
LK
L. Ashok Kumar
Professor Ashok Kumar is Principal at
Thiagarajar College of Engineering, Madurai, India.
His current research focuses on integration of renewable energy systems in the smart grid, biomedical applications, and wearable electronics. He has three years of industrial experience and 24 years of academic and research experience. He is also the author of several books and technical papers in national and international journals.
Affiliations and expertise
Principal Thiagarajar College of Engineering Madurai , IndiaRead Predictive Analytics using MATLAB(R) for Biomedical Applications on ScienceDirect