Predictive analytics using MATLAB(R) for biomedical applications /
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 overvi...
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| Formaat: | Elektronisch E-boek |
| Taal: | Engels |
| Gepubliceerd in: |
London, United Kingdom :
Academic Press, an imprint of Elsevier,
[2025]
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| Onderwerpen: | |
| Online toegang: | ScienceDirect |
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| LEADER | 00000cam a2200000 i 4500 | ||
|---|---|---|---|
| 001 | on1461038475 | ||
| 003 | OCoLC | ||
| 005 | 20260915095213.0 | ||
| 006 | m o d | ||
| 007 | cr cnu---unuuu | ||
| 008 | 241016s2025 enk o 000 0 eng d | ||
| 040 | |a OPELS |b eng |e rda |e pn |c OPELS |d OCLCO |d SFB |d OCLCF |d OCLCO | ||
| 020 | |z 9780443298882 | ||
| 035 | |a (OCoLC)1461038475 | ||
| 050 | 4 | |a R855.2 | |
| 082 | 0 | 4 | |a 610.285/53 |2 23/eng/20241016 |
| 100 | 1 | |a Kumar, L. Ashok, |e author. | |
| 245 | 1 | 0 | |a Predictive analytics using MATLAB(R) for biomedical applications / |c L. Ashok Kumar. |
| 264 | 1 | |a London, United Kingdom : |b Academic Press, an imprint of Elsevier, |c [2025] | |
| 300 | |a 1 online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 520 | |a 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. | ||
| 588 | 0 | |a Online resource; title from PDF title page (ScienceDirect, viewed October 16, 2024). | |
| 505 | 0 | |a Front Cover -- Predictive Analytics Using MATLAB® for Biomedical Applications -- Copyright Page -- Contents -- About the author -- Preface -- Acknowledgments -- About the book -- 1 Introduction to predictive analytics and MATLAB® -- 1.1 Introduction -- 1.1.1 Purpose and scope -- 1.1.2 Audience -- 1.1.3 Prerequisites -- 1.1.4 Catering to different backgrounds -- 1.2 Foundations of biomedical data -- 1.3 Characteristics and challenges of processing biomedical signals -- 1.3.1 Techniques for preprocessing and analyzing biomedical data -- 1.4 Introduction to predictive analytics -- 1.4.1 Data insights and its significance -- 1.4.2 Models and algorithms -- 1.4.3 Types of models -- 1.4.3.1 Classification model -- 1.4.3.2 Clustering model -- 1.4.3.3 Forecast model -- 1.4.3.4 Outliers model -- 1.4.3.5 Time series model -- 1.5 Predictive analytics in healthcare -- 1.5.1 Use cases for predictive analytics in healthcare -- 1.5.2 Benefits of predictive analytics in healthcare -- 1.5.3 Process flow in predictive analytics -- 1.5.3.1 Data collection -- 1.5.3.2 Data preprocessing -- 1.5.3.3 Modeling -- 1.5.3.4 Evaluation -- 1.5.3.5 Deployment -- 1.6 Predictive analytics techniques -- 1.6.1 Regression analysis: linear and logistic regression -- 1.6.1.1 Linear regression -- 1.6.1.2 Logistic regression -- 1.6.2 Time series analysis -- 1.7 Machine learning algorithms -- 1.7.1 Decision trees -- 1.7.2 Random forests -- 1.7.3 Support vector machines -- 1.7.4 Neural networks -- 1.8 Introduction to MATLAB -- 1.8.1 Motivation for using MATLAB in biomedical applications -- 1.8.2 Overview of MATLAB's capabilities for data analysis, modeling, and predictive analytics -- 1.8.3 Getting started with MATLAB -- 1.8.3.1 Data handling and visualization in MATLAB -- 1.8.3.2 Data manipulation techniques -- 1.8.3.2.1 Filtering -- 1.8.3.2.2 Interpolation. | |
| 505 | 8 | |a 1.8.3.2.3 Data transformation -- 1.8.3.3 Visualization tools in MATLAB -- 1.8.3.3.1 Plotting signals -- 1.8.3.3.2 Image display -- 1.8.3.3.3 Graph visualization -- 1.8.3.3.4 3D visualization -- 1.8.3.4 Utilizing MATLAB's plotting functions for effective data representation -- 1.8.3.4.1 Customization -- 1.8.3.4.2 Multiple subplots -- 1.8.3.4.3 Annotations -- 1.8.3.4.4 Exporting figures -- 1.9 Mathematical modeling in biomedical engineering -- 1.9.1 Importance of mathematical modeling in biomedical engineering -- 1.9.2 Basics of differential equations and their role in modeling dynamic systems -- 1.9.2.1 Ordinary differential equations -- 1.9.2.2 Partial differential equations -- 1.9.3 Statistical modeling techniques for analyzing biomedical data variability -- 1.10 Conclusion -- References -- 2 Prognostic insights: predictive analytics in nephrological diseases -- 2.1 Introduction -- 2.2 Kidney disease -- 2.2.1 Types -- 2.2.2 Characteristics of kidney disease -- 2.2.3 Ratio of kidney disease over the years -- 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® -- 2.7.1 Dataset description -- 2.7.2 Statistical analysis on dataset -- 2.7.2.1 Code appendix -- 2.7.2.2 Code description -- 2.7.3 Methodology -- 2.7.4 MATLAB code appendix -- 2.7.4.1 Code description -- 2.8 Results and discussion -- 2.9 Conclusion -- References -- 3 Harnessing predictive analytics for cardiovascular diseases -- 3.1 Introduction -- 3.1.1 Benefits of early prediction of CVD -- 3.1.2 Patient data helpful in CVD risk assessment -- 3.2 Open access datasets -- 3.3 Related work -- 3.4 K-means clustering -- 3.4.1 Working algorithm -- 3.4.2 Value of "K number of clusters" in K-means clustering -- 3.4.3 Elbow method -- 3.4.4 Distance measures. | |
| 505 | 8 | |a 3.4.4.1 Euclidean distance measure -- 3.4.4.2 Squared Euclidean distance measure -- 3.4.4.3 Manhattan distance measure -- 3.4.4.4 Cosine distance measure -- 3.4.5 Advantages of K -means clustering -- 3.4.6 Limitations -- 3.4.7 MATLAB implementation -- 3.4.8 Description of functions used in code -- 3.4.9 Matlab code -- 3.4.10 Output -- 3.5 Decision tree -- 3.5.1 Types of decision trees -- 3.5.2 Attribute selection at each node -- 3.5.3 Algorithm -- 3.5.4 Advantages -- 3.5.5 Limitations -- 3.5.6 MATLAB implementation -- 3.5.7 Statistical analysis of cardiovascular dataset -- 3.5.8 Code explanation -- 3.5.9 Description of functions used in code. | |
| 650 | 0 | |a Biomedical engineering |x Data processing. | |
| 630 | 0 | 0 | |a MATLAB. |
| 650 | 6 | |a Génie biomédical |0 (CaQQLa)201-0021888 |x Informatique. |0 (CaQQLa)201-0380011 | |
| 630 | 0 | 7 | |a MATLAB |2 fast |0 (OCoLC)fst01365096 |
| 650 | 7 | |a Biomedical engineering |x Data processing |2 fast |0 (OCoLC)fst00832570 | |
| 856 | 4 | 0 | |3 ScienceDirect |u https://www.sciencedirect.com/science/book/9780443298882 |