Computational Learning Approaches to Data Analytics in Biomedical Applications

Computational Learning Approaches to Data Analytics in Biomedical Applications provides a unified framework for biomedical data analysis using varied machine learning and statistical techniques.It presents insights on biomedical data processing, innovative clustering algorithms and techniques, and connections between statistical analysis and clustering. The book introduces and discusses the major problems relating to data analytics, provides a review of influential and state-of-the-art learning algorithms for biomedical applications, reviews cluster validity indices and how to select the appropriate index, and includes an overview of statistical methods that can be applied to increase confidence in the clustering framework and analysis of the results obtained.Includes an overview of data analytics in biomedical applications and current challenges Updates on the latest research in supervised learning algorithms and applications, clustering algorithms and cluster validation indices Provides complete coverage of computational and statistical analysis tools for biomedical data analysis Presents hands-on training on the use of Python libraries, MATLAB� tools, WEKA, SAP-HANA and R/Bioconductor Computational Learning Approaches to Data Analytics in Biomedical Applications is written by Khalid Al-Jabery; Tayo Obafemi-Ajayi; Gayla Olbricht; Donald Wunsch and published by Academic Press. ISBNs for Computational Learning Approaches to Data Analytics in Biomedical Applications are 9780128144831, 0128144831 and the print ISBNs are 9780128144824, 0128144823.