Data Mining and Machine Learning for Biomedical Applications

Data Mining and Machine Learning for Biomedical Applications is a rigorous practical introduction to the fundamentals of data science. It discusses topics such as data integration and management; statistical methods of data science; methodological approaches used for data mining and knowledge discovery with biomedical domain examples; the core principles and methods of hypothesis-driven statistical analyses; differences and relative benefits of machine learning approaches; predictive model performance assessment; and concepts of bias and variance with respect to the design and evaluation of predictive models. A final chapter presents considerations and limitations when applying and interpreting data science models in biological science and bioengineering.

For graduate students, this book offers a comprehensive methods introduction, making it ideal to accompany a course in this area. It is also useful for established engineers and scientists who wish to explore data mining or predictive analytics within their domains of expertise. This reference is fully supported with exercises, discussion questions, code vignettes, and code files with demonstration code. This presentation of coded solutions has been prepared with readers in mind who have limited coding experience. The fully coded methods are presented in both R and Python. The foundational principles covered in this book can be applied by readers when creating new tools for diagnosis, monitoring, information visualization, and robotic intervention.
 
Data Mining and Machine Learning for Biomedical Applications is a rigorous practical introduction to the fundamentals of data science. It discusses topics such as data integration and management; statistical methods of data science; methodological approaches used for data mining and knowledge discovery with biomedical domain examples; the core principles and methods of hypothesis-driven statistical analyses; differences and relative benefits of machine learning approaches; predictive model performance assessment; and concepts of bias and variance with respect to the design and evaluation of predictive models. A final chapter presents considerations and limitations when applying and interpreting data science models in biological science and bioengineering.

For graduate students, this book offers a comprehensive methods introduction, making it ideal to accompany a course in this area. It is also useful for established engineers and scientists who wish to explore data mining or predictive analytics within their domains of expertise. This reference is fully supported with exercises, discussion questions, code vignettes, and code files with demonstration code. This presentation of coded solutions has been prepared with readers in mind who have limited coding experience. The fully coded methods are presented in both R and Python. The foundational principles covered in this book can be applied by readers when creating new tools for diagnosis, monitoring, information visualization, and robotic intervention.